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Define the most common research questions in target discovery and validation. Establish an agreement between the project team that these are indeed the core target discovery business questions, and rank order them by vote by perceived relative importance. If such questions are many, pick the top ones. Establish an agreement on how many exactly. One can use this paper as a starting point for listing of relevant competency questions: https://www.sciencedirect.com/science/article/pii/S1359644613001542 (Failure to identify business questions, or picking too many or too few is a project risk)
Open Targets can serve as a publicly available standardized data source for this use case. Validate that Open Targets either has a ready to use Knowledge Graph implementation, or can be converted into a KG with reasonable cost (this is known project risk)
Select a Large Language Model engine from publicly accessible sources. (Failure to identify a suitable open LLM is a project risk)
Prompt-tuning procedure:
Retrieval Augmented Generation (RAG):
Ask plain English question using prompt-tuned version of one of the questions from the business questions collection
This question is converted into a structured query by an LLM (Failure to generate a proper query for a KG database system is a risk)
Execute this query over a structured controlled data source (e.g. Open Targets DB)
Convert raw output of the query into human-readable input by an LLM
An expert compares this answer or answers with the expected one(s)
An accuracy metric is computed + inputs and outputs saved in some DB
Prompt-tune the opening plain text question to maximize the output quality
Repeat this tuning cycle for all business questions in the collection
Historic (obsolete) versions of the above steps:
Historic vision for the query (this is obsolete and is only preserved here for scope traceability):
Define a plain text data source for mining; one of the choices can be the entire set of paper abstracts indexed in PubMed plus perhaps the entire collection of open-source papers. (Failure to download this large volume of data can be a risk)
Ask a plain English question from the collection of business questions identified above
An LLM uses the question and the data source to produce a human-readable answerHistoric vision for the QA (likely not necessary):
Either same or some other LLM converts this answer to a Knowledge Graph. (KG generation from text is a source of risk)
This answer Knowledge Graph is compared to the KG of the original data source (such as Open Targets). This comparison must be local; in other words, irrelevant sections of the larger knowledge graph should not be considered. (Ability to find a ready KG comparison algorithm or to code it fresh is a risk).
Prompt-tune the opening plain text question to maximize the output quality
Repeat this tuning cycle for all business questions in the collection
Use these optimized prompts in RAG, below
Retrieval Augmented Generation (RAG):
Ask plain English question using prompt-tuned version of one of the questions from the business questions collection
This question is converted into a structured query by an LLM (Failure to generate a proper query for a KG database system is a risk)
Execute this query over a structured controlled data source (e.g. Open Targets DB)
Convert raw output of the query into human-readable input by an LLM
Quality assurance: it is desirable to produce a few instances of questions with known answers, ideally, by human experts, and then use these question-answer pairs as sanity checks of the RAG pipeline
Not in scope:
Proprietary data are not used to train any model and project participants are not asked to share proprietary data in any way. But proprietary data may be analyzed in the context of this set-up by the individual participants using private instances of the pipeline.
Training of a brand new LLM is out of scope. The plan is to only prompt-tune an existing LLM.
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Phase | Milestones | Deliverables | Est Date |
Initiation | Project charter |
| 12/11/23 (Complete) |
Elaboration |
|
| Q1 2024 |
Construction |
|
| TBDQ3 2024 |
Transition | Sustainability achieved |
| TBD |
Risk Registry
Risks in green are resolved
Risks in yellow are in active research
Risks in white are general in nature
Description | Mitigation | Responsible Party | |
Failure to identify business questions, or picking too many or too few | Draft appropriate business questions - DONE; but not all business questions can be answered with specific technologies, so must take this factor into account | Lee Harland, John Wise, Bruce Press, Peter Revill | |
| The Hyve Open Targets/EBI:
| ||
| Jon Stevens, Etzard Stolte, Helena Deus; Brian Evarts; Wouter Franke, Matthijs van der Zee | ||
Failure to generate a proper query for a KG database system by an LLM | Technology research.
| The Hyve Open Targets/EBI: Sebastian Lobentanzer Ellen McDonagh | |
Yes in general | The Hyve | ||
Failure to download a large volume of data (all of the PubMed as a maximum) for the prompt-tuning of the LLM | CLOSED RISK This may be unnecessary, TBD | ||
Failure to perform local KG comparison with calculation of a score |
| CLOSED RISK | |
Failure to build a prototypical target discovery pipeline on the limited budget in case of mounting technical difficulties | Schedule the project in phases. Aim to answer known unknowns and to establish risk mitigation strategies early in this phase (“project elaboration”) | ||
Some proprietary LLMs may be censored, thus introducing uncontrollable bias in the answers that they produce |
| Identified and resolved in the LLM sub-team |
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2024.02.07 Recording (Passcode: L58@v7Dg) Slides | Slides from the talk by Sebastian Lobentanzer
2024.03.20 Recording (Passcode: LZ!jZT4z) Slides | Architecture diagram in Draw.io | Architecture diagram PNG file
2024.04.17 Recording (Passcode: Yn2!5qJK) Slides | Slides from the talk by Jon Stevens
2024.08.07 Recording (Passcode: %.1&ukfM) Slides | Includes a talk by Peter Dorr: SPARQL query code generation with LLMs
2024.09.04 Recording (Passcode: t3?B*?CX) Slides | Includes a talk by Oleg Stroganov on agents controlling the actions of LLMs | Slides from the talk by Oleg Stroganov
2024.11.12 Email communication: Slides from the report by Oleg Stroganov
2024.11.20 Recording (Passcode: EE!C54u#) Slides | Slides by Oleg Stroganov with an update
Lessons Learned
The highest risk item is generation of the structured query (Cyphrer or SPARQL) from a plain English request. Some publications estimate success rate of about 48% on the first attempt.
The structure of the database used for queries matters. LLMs can easier produce meaningful structured queries for databases with flat, simple structure.
Practically useful system requires filtering or secondary mining of output in addition to natural language narration.
It is extremely important to implement a reliable named entity recognition system. The same acronym can refer to completely different entities, which can be differentiated either from the context (hard) or by asking clarifying questions. Must also map synonyms. Without these measures naïve queries in a RAG environment will fail.
LLMs may produce different structured queries starting from the same natural language question. These queries may be semantically and structurally correct, but may include assumptions on the limit of the number of items to return, or order, or lack of these. These variations are not deterministic. As a result on different execution rounds the same natural language may result in different answers. It is necessary to explicitly formulate the limits, order restrictions, and other parameters when asking the question, or to determine the user’s intentions in a conversation with a chain of thought. A question related to this topic, is whether specifics in the implementation of usual RAG models with a vector database may introduce implicit restrictions on what data is explored by the LLM and what data is not, and thus artificially limit the answers. This may be happening without the user knowing the restrictions (and perhaps even without the system’s authors knowing that they introduced such restrictions embedded in the specifics of the system architecture).
References
https://www.sciencedirect.com/science/article/pii/S1359644613001542
Open LLM Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
Chatbot Arena: https://chat.lmsys.org/?arena
Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning
Knowledge-Consistent Dialogue Generation with Language Models and Knowledge Graphs
BioChatter Benchmark Results: https://biochatter.org/benchmark-results/#biochatter-query-generation
MBET Benchmark (embeddings) https://huggingface.co/spaces/mteb/leaderboard
Lora-Land and Lorax: https://predibase.com/lora-land
A Benchmark to Understand the Role of Knowledge Graphs on Large Language Model's Accuracy for Question Answering on Enterprise SQL Databases. Summary: queries over a KG with GPT 4 are much more accurate than queries over a SQL database with GPT 4. https://arxiv.org/abs/2311.07509
https://towardsdatascience.com/evaluating-llms-in-cypher-statement-generation-c570884089b3
Kazu - Biomedical NLP Framework: https://github.com/AstraZeneca/KAZU
Zhou, L., Schellaert, W., Martínez-Plumed, F. et al. Larger and more instructable language models become less reliable. Nature 634, 61–68 (2024). https://doi.org/10.1038/s41586-024-07930-y
Karthik Soman, Peter W Rose, John H Morris, Rabia E Akbas, Brett Smith, Braian Peetoom, Catalina Villouta-Reyes, Gabriel Cerono, Yongmei Shi, Angela Rizk-Jackson, Sharat Israni, Charlotte A Nelson, Sui Huang, Sergio E Baranzini, Biomedical knowledge graph-optimized prompt generation for large language models, Bioinformatics, Volume 40, Issue 9, September 2024, btae560, https://doi.org/10.1093/bioinformatics/btae560
References on Named Entity Recognition in biological sciences: Pubmed
Incremental Knowledge Graphs Constructor Using Large Language Models