Agent-based Automated Claim Matching with Instruction-following LLMs

• URL: http://arxiv.org/abs/2510.23924v1
• Date: Mon, 27 Oct 2025 23:09:35 GMT
• Title: Agent-based Automated Claim Matching with Instruction-following LLMs
• Authors: Dina Pisarevskaya, Arkaitz Zubiaga,
• Abstract summary: We propose a two-step pipeline that first generates prompts with LLMs, to then perform claim matching as a binary classification task with LLMs.<n>We demonstrate that LLM-generated prompts can outperform SOTA with human-generated prompts, and that smaller LLMs can do as well as larger ones in the generation process.<n>Our investigation into the prompt generation process in turn reveals insights into the LLMs' understanding of claim matching.
• Score: 10.59972039391162
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a novel agent-based approach for the automated claim matching task with instruction-following LLMs. We propose a two-step pipeline that first generates prompts with LLMs, to then perform claim matching as a binary classification task with LLMs. We demonstrate that LLM-generated prompts can outperform SOTA with human-generated prompts, and that smaller LLMs can do as well as larger ones in the generation process, allowing to save computational resources. We also demonstrate the effectiveness of using different LLMs for each step of the pipeline, i.e. using an LLM for prompt generation, and another for claim matching. Our investigation into the prompt generation process in turn reveals insights into the LLMs' understanding of claim matching.

Related papers

• Neural Bandit Based Optimal LLM Selection for a Pipeline of Tasks [11.389019661082415]
  We propose a neural contextual bandit-based algorithm that trains neural networks that model LLM success on each subtask in an online manner.<n> Experiments on telecommunications question answering and medical diagnosis prediction datasets illustrate the effectiveness of our proposed approach.
  arXiv  Detail & Related papers  (2025-08-13T17:19:41Z)
• Iterative Self-Incentivization Empowers Large Language Models as Agentic Searchers [74.17516978246152]
  Large language models (LLMs) have been widely integrated into information retrieval to advance traditional techniques.<n>We propose EXSEARCH, an agentic search framework, where the LLM learns to retrieve useful information as the reasoning unfolds.<n>Experiments on four knowledge-intensive benchmarks show that EXSEARCH substantially outperforms baselines.
  arXiv  Detail & Related papers  (2025-05-26T15:27:55Z)
• LLM-AutoDiff: Auto-Differentiate Any LLM Workflow [58.56731133392544]
  We introduce LLM-AutoDiff: a novel framework for Automatic Prompt Engineering (APE)<n>LLMs-AutoDiff treats each textual input as a trainable parameter and uses a frozen backward engine to generate feedback-akin to textual gradients.<n>It consistently outperforms existing textual gradient baselines in both accuracy and training cost.
  arXiv  Detail & Related papers  (2025-01-28T03:18:48Z)
• Efficient Dynamic Ensembling for Multiple LLM Experts [44.41847678666002]
  Ensemble reasoning for the strengths of different LLM experts is critical to achieving consistent and satisfactory performance on diverse inputs.<n>We propose an efficient Dynamic Ensemble Reasoning paradigm, called DER, to integrate the strengths of multiple LLM experts conditioned on dynamic inputs.<n> Experiments demonstrate that our method uses fewer computational resources to achieve better performance compared to state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-12-10T12:05:56Z)
• RuAG: Learned-rule-augmented Generation for Large Language Models [62.64389390179651]
  We propose a novel framework, RuAG, to automatically distill large volumes of offline data into interpretable first-order logic rules. We evaluate our framework on public and private industrial tasks, including natural language processing, time-series, decision-making, and industrial tasks.
  arXiv  Detail & Related papers  (2024-11-04T00:01:34Z)
• LLMmap: Fingerprinting For Large Language Models [15.726286532500971]
  With as few as 8 interactions, LLMmap can accurately identify 42 different LLM versions with over 95% accuracy.<n>We discuss potential mitigations and demonstrate that, against resourceful adversaries, effective countermeasures may be challenging or even unrealizable.
  arXiv  Detail & Related papers  (2024-07-22T17:59:45Z)
• RePrompt: Planning by Automatic Prompt Engineering for Large Language Models Agents [27.807695570974644]
  We propose a novel method, textscRePrompt, which does agradient descent"-like approach to optimize the step-by-step instructions in the prompts given to LLM agents.<n>By leveraging intermediate feedback, textscRePrompt can optimize the prompt without the need for a final solution checker.
  arXiv  Detail & Related papers  (2024-06-17T01:23:11Z)
• EnvGen: Generating and Adapting Environments via LLMs for Training Embodied Agents [65.38474102119181]
  We propose EnvGen, a framework to adaptively create training environments. We train a small RL agent in a mixture of the original and LLM-generated environments. We find that a small RL agent trained with EnvGen can outperform SOTA methods, including a GPT-4 agent, and learns long-horizon tasks significantly faster.
  arXiv  Detail & Related papers  (2024-03-18T17:51:16Z)
• LLM Maybe LongLM: Self-Extend LLM Context Window Without Tuning [67.39585115936329]
  We argue that LLMs have inherent capabilities to handle long contexts without fine-tuning. We propose SelfExtend to extend the context window of LLMs by constructing bi-level attention information. We conduct comprehensive experiments on multiple benchmarks and the results show that our SelfExtend can effectively extend existing LLMs' context window length.
  arXiv  Detail & Related papers  (2024-01-02T18:30:51Z)
• LLatrieval: LLM-Verified Retrieval for Verifiable Generation [67.93134176912477]
  Verifiable generation aims to let the large language model (LLM) generate text with supporting documents. We propose LLatrieval (Large Language Model Verified Retrieval), where the LLM updates the retrieval result until it verifies that the retrieved documents can sufficiently support answering the question. Experiments show that LLatrieval significantly outperforms extensive baselines and achieves state-of-the-art results.
  arXiv  Detail & Related papers  (2023-11-14T01:38:02Z)
• Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves [57.974103113675795]
  We present a method named Rephrase and Respond' (RaR) which allows Large Language Models to rephrase and expand questions posed by humans. RaR serves as a simple yet effective prompting method for improving performance. We show that RaR is complementary to the popular Chain-of-Thought (CoT) methods, both theoretically and empirically.
  arXiv  Detail & Related papers  (2023-11-07T18:43:34Z)
• LaGR-SEQ: Language-Guided Reinforcement Learning with Sample-Efficient Querying [71.86163159193327]
  Large language models (LLMs) have recently demonstrated their impressive ability to provide context-aware responses via text. This ability could potentially be used to predict plausible solutions in sequential decision making tasks pertaining to pattern completion. We introduce LaGR, which uses this predictive ability of LLMs to propose solutions to tasks that have been partially completed by a primary reinforcement learning (RL) agent.
  arXiv  Detail & Related papers  (2023-08-21T02:07:35Z)
• Response Length Perception and Sequence Scheduling: An LLM-Empowered LLM Inference Pipeline [22.08897444328099]
  Large language models (LLMs) have revolutionized the field of AI, demonstrating unprecedented capacity across various tasks. In this paper, we propose an efficient LLM inference pipeline that harnesses the power of LLMs.
  arXiv  Detail & Related papers  (2023-05-22T15:36:06Z)

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.