Improving Automatic Evaluation of Large Language Models (LLMs) in Biomedical Relation Extraction via LLMs-as-the-Judge

• URL: http://arxiv.org/abs/2506.00777v1
• Date: Sun, 01 Jun 2025 02:01:52 GMT
• Title: Improving Automatic Evaluation of Large Language Models (LLMs) in Biomedical Relation Extraction via LLMs-as-the-Judge
• Authors: Md Tahmid Rahman Laskar, Israt Jahan, Elham Dolatabadi, Chun Peng, Enamul Hoque, Jimmy Huang,
• Abstract summary: Large Language Models (LLMs) have demonstrated impressive performance in biomedical relation extraction.<n>This paper investigates the use of LLMs-as-the-Judge as an alternative evaluation method for biomedical relation extraction.
• Score: 7.064104563689608
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have demonstrated impressive performance in biomedical relation extraction, even in zero-shot scenarios. However, evaluating LLMs in this task remains challenging due to their ability to generate human-like text, often producing synonyms or abbreviations of gold-standard answers, making traditional automatic evaluation metrics unreliable. On the other hand, while human evaluation is more reliable, it is costly and time-consuming, making it impractical for real-world applications. This paper investigates the use of LLMs-as-the-Judge as an alternative evaluation method for biomedical relation extraction. We benchmark 8 LLMs as judges to evaluate the responses generated by 5 other LLMs across 3 biomedical relation extraction datasets. Unlike other text-generation tasks, we observe that LLM-based judges perform quite poorly (usually below 50% accuracy) in the biomedical relation extraction task. Our findings reveal that it happens mainly because relations extracted by LLMs do not adhere to any standard format. To address this, we propose structured output formatting for LLM-generated responses that helps LLM-Judges to improve their performance by about 15% (on average). We also introduce a domain adaptation technique to further enhance LLM-Judge performance by effectively transferring knowledge between datasets. We release both our human-annotated and LLM-annotated judgment data (36k samples in total) for public use here: https://github.com/tahmedge/llm_judge_biomedical_re.

Related papers

• An Empirical Study of Many-to-Many Summarization with Large Language Models [82.10000188179168]
  Large language models (LLMs) have shown strong multi-lingual abilities, giving them the potential to perform Many-to-many summarization (M2MS) in real applications.<n>This work presents a systematic empirical study on LLMs' M2MS ability.
  arXiv  Detail & Related papers  (2025-05-19T11:18:54Z)
• "I know myself better, but not really greatly": How Well Can LLMs Detect and Explain LLM-Generated Texts? [10.454446545249096]
  This paper investigates detection and explanation capabilities of current LLMs across two settings: binary (human vs. LLM-generated) and ternary classification (including an undecided'' class)<n>We evaluate 6 close- and open-source LLMs of varying sizes and find that self-detection (LLMs identifying their own outputs) consistently outperforms cross-detection (identifying outputs from other LLMs)<n>Our findings underscore the limitations of current LLMs in self-detection and self-explanation, highlighting the need for further research to address overfitting and enhance generalizability.
  arXiv  Detail & Related papers  (2025-02-18T11:00:28Z)
• Can LLMs Replace Human Evaluators? An Empirical Study of LLM-as-a-Judge in Software Engineering [18.766132076075365]
  Large language models (LLMs) have been deployed to tackle various software engineering (SE) tasks like code generation.<n>Pass@k metric requires extensive unit tests and configured environments, and is not suitable for evaluating LLM-generated text.<n>Conventional metrics like BLEU, which measure only lexical rather than semantic similarity, have also come under scrutiny.
  arXiv  Detail & Related papers  (2025-02-10T06:49:29Z)
• Detecting Hallucinations in Large Language Model Generation: A Token Probability Approach [0.0]
  Large Language Models (LLMs) produce inaccurate outputs, also known as hallucinations. This paper introduces a supervised learning approach employing only four numerical features derived from tokens and vocabulary probabilities obtained from other evaluators. The method yields promising results, surpassing state-of-the-art outcomes in multiple tasks across three different benchmarks.
  arXiv  Detail & Related papers  (2024-05-30T03:00:47Z)
• Evaluating the Factuality of Large Language Models using Large-Scale Knowledge Graphs [30.179703001666173]
  Factuality issue is a critical concern for Large Language Models (LLMs) We propose GraphEval to evaluate an LLM's performance using a substantially large test dataset. Test dataset is retrieved from a large knowledge graph with more than 10 million facts without expensive human efforts.
  arXiv  Detail & Related papers  (2024-04-01T06:01:17Z)
• LLMRefine: Pinpointing and Refining Large Language Models via Fine-Grained Actionable Feedback [65.84061725174269]
  Recent large language models (LLM) are leveraging human feedback to improve their generation quality. We propose LLMRefine, an inference time optimization method to refine LLM's output. We conduct experiments on three text generation tasks, including machine translation, long-form question answering (QA), and topical summarization. LLMRefine consistently outperforms all baseline approaches, achieving improvements up to 1.7 MetricX points on translation tasks, 8.1 ROUGE-L on ASQA, 2.2 ROUGE-L on topical summarization.
  arXiv  Detail & Related papers  (2023-11-15T19:52:11Z)
• ReEval: Automatic Hallucination Evaluation for Retrieval-Augmented Large Language Models via Transferable Adversarial Attacks [91.55895047448249]
  This paper presents ReEval, an LLM-based framework using prompt chaining to perturb the original evidence for generating new test cases. We implement ReEval using ChatGPT and evaluate the resulting variants of two popular open-domain QA datasets. Our generated data is human-readable and useful to trigger hallucination in large language models.
  arXiv  Detail & Related papers  (2023-10-19T06:37:32Z)
• Assessing the Reliability of Large Language Model Knowledge [78.38870272050106]
  Large language models (LLMs) have been treated as knowledge bases due to their strong performance in knowledge probing tasks. How do we evaluate the capabilities of LLMs to consistently produce factually correct answers? We propose MOdel kNowledge relIabiliTy scORe (MONITOR), a novel metric designed to directly measure LLMs' factual reliability.
  arXiv  Detail & Related papers  (2023-10-15T12:40:30Z)
• Benchmarking large language models for biomedical natural language processing applications and recommendations [22.668383945059762]
  Large Language Models (LLMs) have shown promise in general domains.<n>We compare their zero-shot, few-shot, and fine-tuning performance with traditional fine-tuning of BERT or BART models.<n>We find issues like missing information and hallucinations in LLM outputs.
  arXiv  Detail & Related papers  (2023-05-10T13:40:06Z)
• Self-Refine: Iterative Refinement with Self-Feedback [62.78755306241981]
  Self-Refine is an approach for improving initial outputs from large language models (LLMs) through iterative feedback and refinement. We evaluate Self-Refine across 7 diverse tasks, ranging from dialog response generation to mathematical reasoning, using state-of-the-art (GPT-3.5, ChatGPT, and GPT-4) LLMs. Our work demonstrates that even state-of-the-art LLMs like GPT-4 can be further improved at test time using our simple, standalone approach.
  arXiv  Detail & Related papers  (2023-03-30T18:30:01Z)
• Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback [127.75419038610455]
  Large language models (LLMs) are able to generate human-like, fluent responses for many downstream tasks. This paper proposes a LLM-Augmenter system, which augments a black-box LLM with a set of plug-and-play modules.
  arXiv  Detail & Related papers  (2023-02-24T18:48:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.