Patent Application
20250211614
The invention relates to computer security, and in particular to preventing online fraud such as phishing, among others.
Online fraud, especially in the form of phishing and identity theft, has been posing an increasing threat to Internet users worldwide. Sensitive identity information such as user names, IDs, passwords, social security and medical records, bank and credit card details obtained fraudulently by international criminal networks operating on the Internet are used to withdraw private funds and/or are further sold to third parties. Beside direct financial damage to individuals, online fraud also causes a range on unwanted side effects, such as increased security costs for companies, higher retail prices and banking fees, declining stock values, lower wages and decreased tax revenue.
Online fraud is facilitated by the explosive growth of mobile computing and online services, with millions of devices such as smartphones and tablet computers constantly connected to the Internet and acting as potential targets. In a typical example of phishing, a user receives a fraudulent communication masquerading as a legitimate message from a service provider such as a bank, phone company, online retailer, etc. The message may report a fictitious problem with the user's account or recent order and invite the user to contact the respective service provider via a link included in the respective message. The link may lead to a fake interface (e.g., webpage) used by online criminals to steal sensitive data such as login credentials and credit card numbers, among others. Accessing such links may further expose the user to a risk of installing malicious software.
Various security software may be used to detect fraudulent webpages and/or phishing messages. However, using such software may require installing a local security agent on the user's computing device, and may further require a certain level of knowledge about online communications, computer security, and/or types of online threats, which is expected to exceed that of an ordinary user. Furthermore, the methods used by cybercriminals to trick users into revealing sensitive information are continuously changing, so users and systems need to constantly adapt.
Modern anti-fraud systems and methods are increasingly relying on artificial intelligence (AI), and in particular on generative language models such as ChatGPT® from OpenAI, Inc., among others. A typical example of such methods comprises formulating a language model prompt to include a text sample (e.g., a suspect message received by a client), and feeding the respective prompt as input to a language model. The model may then process the respective prompt and reply with another text indicating whether the respective text sample is indicative of fraud. However, such approaches have so far proven less reliable than classical anti-fraud methods. Typical language models are pre-trained on a generic corpus of text, and therefore largely lack specific knowledge of online fraud. Furthermore, designing, training, and operating large language models require a hefty investment in computational resources and know-how, so developing such a model explicitly for fraud detection may fail a cost-benefit analysis. Therefore, there is an ongoing interest in developing reliable, cost-effective, and user-friendly methods of using AI in combating online fraud.
According to one aspect, a computer system comprises at least one hardware processor configured to execute a chatbot configured to output a verdict formulated in a natural language and indicating whether a received target message is indicative of fraud. The chatbot comprises a prompt manager communicatively coupled to a generative language module (GLM). The GLM is configured to receive from the prompt manager a prompt formulated in the natural language, and in response, output to the prompt manager a predicted token comprising a likely continuation of the received prompt. The prompt manager is configured to formulate the prompt to instruct the GLM to perform a fraud detection task according to the target message. The prompt manager is further configured to determine whether the predicted token comprises a pre-determined flag token, and in response, if the predicted token comprises the flag token, initiate an execution of a code snippet, wherein executing the code snippet causes an update of the prompt. The prompt manager is further configured to transmit the updated prompt to the GLM, and determine the verdict according to an output produced by the GLM in response to the updated prompt.
According to another aspect, a computer-implemented fraud detection method comprises employing at least one hardware processor to execute a chatbot configured to output a verdict formulated in a natural language and indicating whether a received target message is indicative of fraud. The chatbot comprises a prompt manager communicatively coupled to a GLM. The GLM is configured to receive from the prompt manager a prompt formulated in the natural language, and in response, output to the prompt manager a predicted token comprising a likely continuation of the received prompt. Executing the prompt manager comprises formulating the prompt to instruct the GLM to perform a fraud detection task according to the target message. Executing the prompt manager further comprises determining whether the predicted token comprises a pre-determined flag token, and in response, if the predicted token comprises the flag token, initiating an execution of a code snippet, wherein executing the code snippet causes an update of the prompt. Executing the prompt manager further comprises transmitting the updated prompt to the GLM, and determining the verdict according to an output produced by the GLM in response to the updated prompt.
According to another aspect, a non-transitory computer-readable medium stores instructions which, when executed by at least one hardware processor of a computer system, cause the computer system to form a chatbot configured to output a verdict formulated in a natural language and indicating whether a received target message is indicative of fraud. The chatbot comprises a prompt manager communicatively coupled to a GLM. The GLM is configured to receive from the prompt manager a prompt formulated in the natural language, and in response, output to the prompt manager a predicted token comprising a likely continuation of the received prompt. The prompt manager is configured to formulate the prompt to instruct the GLM to perform a fraud detection task according to the target message. The prompt manager is further configured to determine whether the predicted token comprises a pre-determined flag token, and in response, if the predicted token comprises the flag token, initiate an execution of a code snippet, wherein executing the code snippet causes an update of the prompt. The prompt manager is further configured to transmit the updated prompt to the GLM, and determine the verdict according to an output produced by the GLM in response to the updated prompt.
The foregoing aspects and advantages of the present invention will become better understood upon reading the following detailed description and upon reference to the drawings where:
In the following description, it is understood that all recited connections between structures can be direct operative connections or indirect operative connections through intermediary structures. A set of elements includes one or more elements. Any recitation of an element is understood to refer to at least one element. A plurality of elements includes at least two elements. Any use of ‘or’ is meant as a nonexclusive or. Unless otherwise required, any described method steps need not be necessarily performed in a particular illustrated order. A first element (e.g., data) derived from a second element encompasses a first element equal to the second element, as well as a first element generated by processing the second element and optionally other data. Making a determination or decision according to a parameter encompasses making the determination or decision according to the parameter and optionally according to other data. Unless otherwise specified, an indicator of some quantity/data may be the quantity/data itself, or an indicator different from the quantity/data itself. A computer program is a sequence of processor instructions carrying out a task. Computer programs described in some embodiments of the present invention may be stand-alone software entities or sub-entities (e.g., subroutines, libraries) of other computer programs. A database or knowledgebase herein denotes any organized, searchable collection of data. Computer-readable media encompass non-transitory media such as magnetic, optic, and semiconductor storage media (e.g., hard drives, optical disks, flash memory, DRAM), as well as communication links such as conductive cables and fiber optic links. According to some embodiments, the present invention provides, inter alia, computer systems comprising hardware (e.g., one or more processors) programmed to perform the methods described herein, as well as computer-readable media encoding instructions to perform the methods described herein.
The following description illustrates embodiments of the invention by way of example and not necessarily by way of limitation.
In some embodiments, chatbot 10 comprises an artificial intelligence (AI) system configured to carry out a conversation (i.e., exchange of messages) with a user in a natural language (NL) such as English or Chinese, among others. Chatbot 10 may be further configured to determine according to a content of the respective conversation whether the user is confronted with a computer security threat, such as online fraud, malware, etc. Alternatively or additionally, chatbot 10 may perform other services such as automated classification of online content (e.g., detection of online propaganda, fake news, and AI-generated content), among others. Some embodiments of chatbot 10 are further configured to provide various other information to the user, for instance answer general questions and offer advice on various computer security subjects such as malicious software, spam, communication privacy, securing online payments, parental control, etc. Chatbot 10 may further advise the user on purchasing computer security software, manage the user's subscriptions to various computer security services, answer billing questions, or in any other way act as a user-friendly interface between the user and a computer security service provider.
In an exemplary fraud prevention scenario illustrated in
In some embodiments, chatbot 10 interacts with human users via a user interface displayed on a front-end device 12 (
In other exemplary embodiments, the user may interact with chatbot 10 via communication interfaces of an online messaging application executing on front-end device 12. Online messaging herein encompasses peer-to-peer messaging as well as messaging via public chatrooms, forums, social media sites, etc. Examples of online messaging include an exchange of short message service (SMS) messages, a sequence of e-mail messages, and a sequence of messages exchanged via instant messaging applications such as WhatsApp Messenger®, Telegram®, WeChat®, and Facebook® Messenger®, among others. Other exemplary online messaging include a content of a Facebook® wall, a chat conducted on an online forum such as Reddit® and Discord®, and a set of comments to a blog post. Exemplary online messaging applications according to embodiments of the present invention include client-side instances of mobile applications such as WhatsApp®, Facebook®, Instagram®, SnapChat® etc., as well as software executing the server side of the respective messaging operations. Other examples of online messaging applications include an email client and an instance of an Internet browser.
For clarity, the present description will focus on communication interfaces that enable the user to carry out a natural language conversation by typing. In other words, exchanges between the user and security chatbot 10 described herein are predominantly in text form. However, a skilled artisan will know that this aspect is not meant to be limiting. The described systems and methods may be adapted to processing of audio messages (spoken conversations), video messages, or any combination of carrier media. In such embodiments, chatbot 10 may be configured to process the respective type of input directly, or alternatively, to convert the type of input provided by the user into text before applying some of the methods described herein. Furthermore, in some embodiments, a communication interface as described herein may enable the user to attach various types of media files (e.g., an image/screenshot, an audio file such as a recorded voice message, etc.) to a text message.
Some embodiments as illustrated in
The actual data exchanged during messaging may vary in format according to the respective messaging platform, protocol, and/or application, but in general such may comprise an encoding of a text and/or an encoding of a media file (e.g., image, movie, sound, etc.). The text part may comprise text written in a natural language, as well as other alphanumeric and/or special characters such as emoticons, among others. An encoding of messages 15-16 may further include identifiers of a sender and receiver of the respective message and a timestamp indicative of a time of transmission of the respective message. Such metadata may enable chatbot 10 to associate each message with an ongoing conversation, and to maintain a conversation context for each conversation, for instance by arranging messages in sequence according to their respective timestamps.
Some embodiments of chatbot 10 may maintain a plurality of concurrent conversations with various users on various subjects. Internally, chatbot 10 may represent each conversation as a separate data structure (e.g., an object with multiple data fields) identified by a unique conversation ID. A conversation object may be formulated according to any data standard known in the art, and may include a user_ID identifying front-end device 12 and/or an individual user of the respective device. The conversation object may further include a plurality of message indicators, each corresponding to an individual message exchanged within the respective conversation. Each individual message indicator may in turn include an identifier of a sender and/or of a receiver, a text content of the respective message, and a timestamp indicating a moment in time when the respective message was sent and/or received. In an alternative embodiment, a conversation object may comprise a concatenation of the text content of all messages in the respective conversation, individual messages arranged in the order of transmission according to their respective timestamp. Message indicators may further include a set of media indicators, for instance copies of an image/video/audio file attached to the respective message, or a network address/URL where the respective media file is located. Some embodiments keep a conversation alive as long as its count of messages does not exceed a predetermined value, as long as the time elapsed since its first message does not exceed a predetermined time threshold, and/or as long as a time elapsed since its latest message does not exceed another predetermined time threshold.
To carry out natural language conversations with human users, some embodiments of chatbot 10 (
The operation of GLM 40 is illustrated in
Exemplary tokens 24a-d and 26 may comprise individual words, but also numbers, punctuation marks, special characters, abbreviations, initialisms (e.g., LOL, ROFL), emojis, as well as network addresses, universal record identifiers (URI) and locators (URL), among others. Some tokens may include multiple words such as phrases, etc. Some GLMs 40 are further configured to input fragments of computer code. For instance, individual tokens 24a-d and 26 may include computer instructions, variable names and values, mathematical symbols, etc.
In a step 102 (
Next, a step 110 may compute token embeddings for all tokens of the initial LM prompt. An embedding herein denotes an internal representation of an individual token, comprising a set of coordinates of the respective token in an abstract multidimensional space typically referred to as an embedding space. The size (count of dimensions) of a typical embedding space ranges between several thousand and several tens of thousands. In an exemplary embodiment using a deep neural network, GLM 40 comprises a sandwich of interconnected neural network layers. A first subset of these layers collectively operate as an encoder, taking each token of LM prompt 22 as input and calculating an embedding of the respective token, i.e., projecting each respective token into the embedding space. In typical embodiments, step 110 comprises a matrix multiplication between the numerical representation of a respective token determined in step 108 and a matrix of synapse weights pre-determined during training of GLM 40. In some embodiments, step 110 further determines positional embeddings indicative of a position of each token 24a-d within LM prompt 22.
Following initialization, tokenization, and embedding, GLM 40 may iterate through a series of consecutive inferences, until some termination condition is satisfied (a step 114 returns a YES). At each inference, a step 116 may determine a predicted token (see token 26 in
In some embodiments, LM prompt 22 is updated in between consecutive inference steps, and a new cycle is started with step 102. However, when not dealing with an initial LM prompt, some embodiments may save computational resources by re-using some of the token embeddings already calculated in previous inference steps. As illustrated in
In conventional language model applications as illustrated in
In contrast to such conventional prompting, in some embodiments of the present invention, LM prompt 22 may be updated in-between consecutive inference steps in ways that differ from and may be more substantial than merely appending the current predicted token. Exemplary modifications to LM prompt 22 include insertions, replacements, and deletions of selected tokens. Tokens may be inserted at various positions within LM prompt 22, as opposed to the end. Furthermore, the type (e.g., insertion, deletion) and content (actual inserted and/or deleted tokens) of prompt modifications are determined by calculations performed outside of the GLM itself. Some explicit examples are given below. Also, crucially, modifications to LM prompt 22 are done inline, i.e., on an already ingested prompt, without re-initializing GLM 40 (as opposed to submitting an entirely new prompt and causing a re-initialization of GLM 40). Some embodiments thus manage to extend the functionality and improve the performance of a conventional chatbot, while benefitting from computational savings as described above.
In some embodiments, prompt manager 30 (
LM prompt 22f further includes a set of flag tokens 29a-b. Flag tokens herein denote specific tokens whose presence in the output of GLM 40 (i.e., in predicted token sequence 27) is detected and interpreted by prompt manager 30 as further detailed below. Exemplary flag tokens include a specific word, keyword, or character sequence (e.g., flag token 29b), a specific sequence of words, an attribute-value pair (e.g., flag token 29a), and a tuple of attribute values, among others. In alternative embodiments, flag tokens may be identified according to whether they include a predetermined special character, such as #, $, etc. In some embodiments as illustrated, instructions 19 further instruct GLM 40 to output at least one indicated flag token in response to, or as part of executing an indicated task. Some flag tokens such as token 29b in
In some embodiments, detection of a flag token within the output of GLM 40 causes prompt manager 30 to initiate an execution of a specific code snippet (i.e., computer program) associated with the respective flag token. Some embodiments as illustrated in
Code snippets as described herein may implement any procedure useful in detecting online fraud, from simple text manipulations such as inserting tokens into and/or deleting tokens from the LM prompt, to extracting data from the LM prompt and/or output token sequence 27 and combining such data with other fraud-indicative characteristics of the text message, such as an identity or an address of the sender, a timestamp of the message, an indicator of whether the message includes a hyperlink, etc. A definitive verdict may then be passed back to GLM 40 with instructions on generating explanations, user advice, information about a particular type of fraud, etc.
Steps 128-130 transmit LM prompt 22 to generative language module 40 and initiate execution of an inference step. When the termination condition is satisfied, a sequence of steps 136-138 may formulate and output reply message 16, which may include at least a part of the current predicted token sequence 27. When the result of the current fraud detection procedure indicates a likelihood of fraud, reply message 16 may further include an explanation or description of the respective type of scam, and a set of recommendations, instructions, and/or advice for the user on countering or mitigating the respective threat. Conversely, when the target message is deemed benign, reply message 16 may include advice on avoiding online fraud.
When the termination condition is not satisfied (step 132 returns a NO), in a step 134 prompt manager 30 may update LM prompt 22 according to the current output of GLM 40, e.g., according to the current predicted token 26.
If predicted token sequence 27 (i.e., the output of GLM 40) includes at least one flag token, a step 148 may identify at least one code snippet according to the respective flag token, e.g., by looking up instruction cache 32 (see description above in relation to
In a step 152, prompt manager 30 may formulate a set of supplemental tokens for insertion into LM prompt 22 according to a result of executing the identified code snippet. Exemplary supplemental tokens may include a new set of LM instructions for GLM 40, various parameter values calculated by the respective code snippet, and other flag tokens, among others. Some examples are given below. A further step 154 may then insert the respective supplemental tokens into LM prompt 22. In some embodiments wherein a flag token identified in step 144 acts as a placeholder, supplemental tokens are inserted into LM prompt 22 precisely at the position of the respective flag token. In some embodiments, a step 156 may then delete the respective flag token and/or other tokens (as indicated in the respective code snippet) from predicted token sequence 27 and/or from LM prompt 22.
The sequence of steps illustrated in
The disclosure above mainly describes a fraud detection system wherein code snippets for manipulating LM prompt 22 are pre-determined and pre-loaded into security chatbot 10. However, in alternative embodiments, code snippets as described herein may be received as part of the input to chatbot 10 as illustrated in
An exemplary logical prompt 20a according to some embodiments of the present invention is illustrated in
Code snippets 25 may be developed and kept up to date by computer security operators and stored within a code repository available to prompt creator 18. Different code snippets may be developed for different types or categories of target message, for different tasks (e.g., fraud detection vs. detection of fake news, etc.), as well as for different users or user categories (e.g., according to a service subscription, according to a geographical location of the respective user, etc.).
When all code snippets in logical prompt 20 have been processed (a step 170 returns a NO), prompt manager 30 may proceed to feeding the initial LM prompt to GLM 40. Prompt manager 30 may then cycle through a sequence of steps 178-184 for each of a plurality of inference steps, until a termination condition is satisfied (e.g., until a step 182 returns a YES). A step 180 may initiate execution of an individual inference step by GLM 40, causing the output of a predicted token. If the termination condition is not satisfied, in a step 184 prompt manager 30 may update the LM prompt according to the respective predicted token(s). Execution of step 184 may comprise the exemplary steps illustrated in
Processor(s) 82 comprise a physical device (e.g. microprocessor, multi-core integrated circuit formed on a semiconductor substrate) configured to execute computational and/or logical operations with a set of signals and/or data. Such signals or data may be encoded and delivered to processor(s) 82 in the form of processor instructions, e.g., machine code.
Processor(s) 82 are generally characterized by an instruction set architecture (ISA), which specifies the respective set of processor instructions (e.g., the x86 family vs. ARM® family), and the size of registers (e.g., 32 bit vs. 64 bit processors), among others. The architecture of processor(s) 82 may further vary according to their intended primary use. While central processing units (CPU) are general-purpose processors, graphics processing units (GPU) may be optimized for image/video processing and parallel computing, such as the implementation of some neural network architectures. Processors 82 may further include application-specific integrated circuits (ASIC), such as Tensor Processing Units (TPU) from Google®, Inc., and Neural Processing Units (NPU) from various manufacturers. TPUs and NPUs may be particularly suited for AI applications as described herein. For instance, selected parts of GLM 40 may execute on a GPU or NPU.
Memory unit 83 may comprise volatile computer-readable media (e.g. dynamic random-access memory—DRAM, GPU memory) storing data/signals/instruction encodings accessed or generated by processor(s) 82 in the course of carrying out operations. Input devices 84 may include computer keyboards, mice, and microphones, among others, including the respective hardware interfaces and/or adapters allowing a user to introduce data and/or instructions into computer system 80. Output devices 85 may include display devices such as monitors and speakers among others, as well as hardware interfaces/adapters such as graphic cards, enabling the respective computing appliance to communicate data to a user. In some embodiments, input and output devices 84-85 share a common piece of hardware (e.g., a touch screen). Storage devices 86 include computer-readable media enabling the non-volatile storage, reading, and writing of software instructions and/or data. Exemplary storage devices include magnetic and optical disks and flash memory devices, as well as removable media such as CD and/or DVD disks and drives. Network adapter(s) 87 comprise specialized hardware that enable computer system 80 to connect to an electronic communication network and/or to other devices/computer systems for data transmission and reception.
Controller hub 90 generically represents the plurality of system, peripheral, and/or chipset buses, and/or all other circuitry enabling the communication between processor(s) 82 and the rest of the hardware components of computer system 80. For instance, controller hub 90 may comprise a memory controller, an input/output (I/O) controller, and an interrupt controller. Depending on hardware manufacturer, some such controllers may be incorporated into a single integrated circuit, and/or may be integrated with processor(s) 82. In another example, controller hub 90 may comprise a northbridge connecting processor 82 to memory 83, and/or a southbridge connecting processor 82 to devices 84, 85, 86, and 87.
The exemplary systems and methods described above enable efficiently using AI systems such as large language models (LLM) for complex specialized tasks such as protecting users against online fraud, among others.
In contrast to many conventional anti-fraud solutions, some embodiments of the present invention employ a chatbot to interface with the user in a friendly, conversational manner. The chatbot may assist the user with a variety of tasks, such determining whether the user is subject to an online threat such as phishing, advising the user on computer security issues, answering questions about subscriptions, accounts, billing, etc. In some embodiments, the chatbot impersonates a user of a popular messaging or social media platform such as Facebook® or WhatsApp Messenger® and so is accessible via a user interface of the respective applications. An exemplary user interface as described is illustrated in
Chatbots implementing large language models (LLM) are rapidly becoming a popular technical solution for interacting with users in a broad variety of situations and applications. The advantages include extending the reach of a target technology to users that lack a technical or computing background, and a reduction of operational costs by replacing human customer care operators with AI agents. Some advanced chatbots such as ChatGPT® from OpenAI, Inc. are capable of answering computer security questions and analyzing data to determine whether a user is targeted by a computer security threat. However, studies have shown that such chatbots sometimes provide wrong or misleading answers, or answers that strongly depend on how the question is formulated. More importantly, their grasp of highly specific issues such as computer security is only as good as the training corpus they have ingested. In other words, if the training corpus does not include training examples relevant to a specific question or situation, the respective chatbot may not return a correct answer or assessment. This problem is especially acute in the area of computer security, wherein methods employed by malicious software and online scammers are continuously changing. Generic training corpora and methodologies are therefore relatively unlikely to keep up with the threat landscape.
In principle, a pre-trained LLM may be further trained to specifically address computer security questions, for instance using a purposely built and maintained corpus of text including examples of online fraud such as known phishing attempts delivered via email messages, SMS, and social media platforms. However, even though such additional training is likely to increase the performance of the respective LLM in detecting online fraud, training LLMs typically incurs substantial computational costs. Furthermore, the additional training does not solve a fundamental problem, namely that LLM are enormously complex systems typically having billions of tunable parameters, and whose behavior is essentially opaque and unpredictable.
Current LLM-based chatbots have also been shown to be vulnerable to malicious manipulation, commonly known in the art as adversarial attacks. Typical examples include carefully formulating the input to an LLM to cause it to fail, as in producing the wrong output, unexpected output (also known as a hallucination), or no output at all.
In addition to the above shortcomings, several computer experiments have revealed that typical LLM-based chatbots often fail to correctly solve more complex problems and/or logical operations such as navigating a decision tree with many possible outcomes depending on the contents of the input. Solving such a problem in a conventional way involves including all instructions for navigating the decision tree (e.g., distinguishing among all possible cases) in one single prompt. However, the LLM may fail to follow such complicated instructions, mainly because of its limited attention span. For instance, it may follow instructions selectively and unpredictably, leading to conclusions which are logically unsound, conflicting, or plain wrong. Furthermore, some commercial LLM services charge according to the length (i.e., count of tokens) of the prompt, so a relatively long and complex prompt may incur substantial costs.
Various workarounds have been proposed to solve this issue. One exemplary strategy known as prompt chaining comprises presenting the LLM with a sequence of individual prompts, each prompt formulated according to a response by the LLM to a previous prompt. In the example of a decision tree, each individual prompt may represent a single branch or branch point. Such prompts may be generated dynamically, according to the characteristics of the target text and according to replies to previous prompts. In other words, the decision process may be steered preferentially along a specific path of traversing the decision tree, wherein each step of the decision process is determined by the outcome of a previous step. However, in conventional prompt chaining, each successive prompt is treated as a separate new submission, ending up incurring substantial costs when the respective LLM charges per prompt.
In contrast to such conventional solutions, some embodiments enable a dynamic inline updating of the input to the respective language model. This allows submitting a single prompt to the LLM and subsequently performing gradual adjustments to the respective prompt according to LLM outputs, without re-initializing the entire language model. In one such example, an initial formulation of the prompt may instruct the LLM to carry out a first task. The prompt may be then updated inline, to instruct the LLM to carry out another task selected according to the output of the first task. Conventional LLMs already perform an inline updating of the input prompt, typically by appending the latest output of the LLM to the existing input prompt. In contrast to such conventional prompt updating, some embodiments update the input prompt more substantially and at positions within the respective prompt other than the end. Some parts of the prompt may be removed, while new content may be added. Also, in embodiments, the content of such prompt modifications is determined according to a result of computations executed outside of the LLM itself.
Some embodiments further submit a set of prompt-editing instructions in the form of code snippets embedded directly in the chatbot prompt. While some conventional LLMs are capable of receiving, executing, and otherwise manipulating computer code, in embodiments of the present invention the code snippets embedded in the input prompt are not meant for execution by the LLM itself, but instead are used for dynamically updating the LLM prompt. In exemplary embodiments, a prompt manager distinct from the computer module implementing the LLM is charged with executing the prompt-editing instructions. The prompt manager may remove such code snippets from the input prompt before submitting the respective prompt to the LLM itself. Bundling up an LLM prompt together with prompt-editing instructions into one logical prompt as described herein has multiple advantages over conventional prompt management. For instance, logical prompts improve portability and facilitate readability and code maintenance by keeping all resources in one place.
One substantial advantage of systems and methods presented herein is that they enable a more efficient solving of problems involving complex computations and/or complex logical operations, compared to conventional approaches using multiple individual LLM prompts. Mainly, they allow solving such problems using one single and relatively short LLM prompt, which may substantially reduce financial and computational costs associated with using the respective LLM. The proposed approach allows, for instance, using an out-of-the-box generic pre-trained LLM to perform computations of arbitrary scope and complexity, wherein the linguistic and generalization powers of the LLM can be combined with additional calculations performed by the prompt manager and/or any other computation module, software, or service. In one such example directed at prevention of online fraud, the LLM may be used to extract a set of features or preliminary verdicts about a target text, which may then be passed on to dedicated anti-fraud software, which produces a definitive verdict.
Substantial reductions in computational costs enabled by embodiments of the present invention come from avoiding having to recompute internal representations of individual tokens of the LLM prompt, relying on the observation that the inline updating described herein preserves the majority of existing prompt tokens (and thus most of the internal state of the LLM). Typical prompts consist of hundreds or thousands of individual tokens. An inline update of the prompt as described herein may only change a few tokens at a time, realistically amounting to a few percent of the entire LLM prompt. Therefore, once the LLM computes internal representations/embeddings of tokens belonging to the initial LLM prompt, ulterior changes to the respective prompt may only require relatively minor updates to the internal LLM state. In contrast, submitting an entirely new LLM prompt typically resets the entire internal state of the LLM, which may require re-computing hundreds or thousands of token embeddings. Other savings in time and costs come from avoiding redundant transmission of data between the LLM and other components of the system. When the LLM is accessed remotely, for instance as a web service, such savings may be substantial.
Although most of the disclosure above was directed at detecting online fraud, an artisan will know that the described systems and methods can be adapted to other applications that extend beyond the traditional scope of computer security, such as detection of bullying, hate speech, fake news, and AI-generated content, among others. Such embodiments may use a single, dynamically evolving LM prompt to instruct GLM 40 to perform a sequence of tasks specific to the respective application. The output of each task may be signaled via a task-specific flag token. Each flag token may in turn trigger the execution of a token-specific code snippet that causes an inline update of the LM prompt, as described in detail above.
Exemplary logical prompt 20c in
It will be clear to one skilled in the art that the above embodiments may be altered in many ways without departing from the scope of the invention. Accordingly, the scope of the invention should be determined by the following claims and their legal equivalents.
This application claims the benefit of the filing date of U.S. provisional patent application No. 63/612,405, filed on Dec. 20, 2023, titled “Dynamic Inline Editing of Language Model Prompts,” the content of which is incorporated by reference herein.
| Number | Date | Country | |
|---|---|---|---|
| 63612405 | Dec 2023 | US |
