GAVEL: Evidence-Contract Debate with Mechanized Scrutiny for Provenance-Grounded Fact-Checking (2026.findings-acl)
Copied to clipboard
| Challenge: | Evidence-grounded fact-checking requires predicting claim veracity while returning faithful evidence at fine granularity. |
| Approach: | They propose a multi-agent debate framework that enforces evidence grounding throughout inference. |
| Outcome: | The proposed framework improves provenance-aware metrics over existing frameworks. |
Similar Papers
InteGround: On the Evaluation of Verification and Retrieval Planning in Integrative Grounding (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing grounding approaches work well for simple queries, but many real-world information needs require synthesizing multiple pieces of evidence. |
| Approach: | They introduce "integrative grounding" to evaluate the ability to ground large language models in external knowledge sources. |
| Outcome: | The proposed approach is robust to redundant evidence, but rationalizes using internal knowledge when information is incomplete. |
MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents (2024.emnlp-main)
Copied to clipboard
| Challenge: | Current methods for fact-checking are based on verifying each piece of a model against potential evidence using an LLM. |
| Approach: | They propose a method that builds small fact-checking models that have GPT-4-level performance but 400x lower cost. |
| Outcome: | The proposed model outperforms other models and reaches GPT-4 accuracy. |
SELENE: Selective and Evidence-Weighted LLM Debating for Efficient and Reliable Reasoning (2026.eacl-industry)
Copied to clipboard
| Challenge: | Existing multi-agent debate frameworks are computationally expensive and prone to degradation under pro-longed debates due to redundant exchanges and unstable judging. |
| Approach: | They propose a framework that unifies Selective Debate Initiation (SDI) with Evidence Weighted Self-Consistency (EWSC) for adaptive, debate-on-demand reasoning. |
| Outcome: | Evaluated on BoolQ, CosmosQA, and an internal QnA benchmark, the proposed framework achieves higher factual robustness and efficiency. |
Complex Claim Verification with Evidence Retrieved in the Wild (2024.naacl-long)
Copied to clipboard
| Challenge: | Prior work makes simplifying assumptions in retrieval that depart from real-world use cases: no access to evidence, access to curated evidence, or access to published evidence after a claim was made. |
| Approach: | They propose a pipeline to check claims using raw evidence from the web . they restrict their retriever to only search documents available prior to the claim's making . |
| Outcome: | The proposed method is based on a political claim dataset and shows that the evidence summary produced by the system is reliable and relevant to answering key questions. |
ReviewGrounder: Improving Review Substantiveness with Rubric-Guided, Tool-Integrated Agents (2026.acl-long)
Copied to clipboard
Zhuofeng Li, Yi Lu, Dongfu Jiang, Haoxiang Zhang, Yuyang Bai, Chuan Li, Yu Wang, Shuiwang Ji, Jianwen Xie, Yu Zhang
| Challenge: | Rapid rise in AI conference submissions has driven increasing exploration of large language models (LLMs) for peer review support. |
| Approach: | They propose a peer review benchmarking tool based on paper-specific rubrics and a rubric-guided framework that decomposes reviewing into drafting and grounding stages. |
| Outcome: | The proposed framework outperforms baselines with stronger/larger backbones in both alignment with human judgments and rubric-based review quality across 8 dimensions. |
Explainable Claim Verification via Knowledge-Grounded Reasoning with Large Language Models (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing claims verification models rely on annotated data, which is expensive to create at a large scale. |
| Approach: | They propose a model that can verify complex claims without annotated data . they leverage the in-context learning ability of Large Language Models to translate a claim into a First-Order-Logic clause . |
| Outcome: | The proposed model outperforms baseline models on three datasets . it performs well on the datasets, and the results are published online. |
Pause or Fabricate? Training Language Models for Grounded Reasoning (2026.findings-acl)
Copied to clipboard
Yiwen Qiu, Linjuan Wu, Yizhou Liu, Yuchen Yan, Jin Ma, Xu Tan, Yao Hu, Daoxin Zhang, Wenqi Zhang, Weiming Lu, Jun Xiao, Yongliang Shen
| Challenge: | Large language models implicitly fabricate information when inputs are incomplete, causing confidence but unreliable conclusions. |
| Approach: | They propose a framework for grounded reasoning under incomplete information that decomposes reasoning into two stages . they propose stage-specific rewards to penalize hallucinations, enabling models to detect gaps, stop proactively, and resume reasoning after clarification. |
| Outcome: | The proposed framework improves premise detection and task success by 30% . it also reduces average response length by over 20% . |
Teaching Language Models to Check Grounded Claim Factuality with Human Test-Taking Strategies (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods for factuality checking require dataset-specific threshold tuning, while LLM-based approaches often use direct prompting. |
| Approach: | They propose to use a reading comprehension task to check for true/false claim factuality and prompt LLMs with explicit test-taking strategies for efficient reasoning. |
| Outcome: | The proposed method reduces token usage by over 80% compared to unguided open-ended reasoning and achieves competitive performance to more expensive alternatives. |
VerifiNER: Verification-augmented NER via Knowledge-grounded Reasoning with Large Language Models (2024.acl-long)
Copied to clipboard
| Challenge: | Recent approaches in domain-specific named entity recognition (NER) have shown remarkable advances, but they still lack faithfulness, producing erroneous predictions. |
| Approach: | They propose a framework that revises errors from existing NER methods using knowledge to produce more faithful predictions. |
| Outcome: | The proposed framework can validate errors from existing models as a model-agnostic approach. |
HypER: Literature-grounded Hypothesis Generation and Distillation with Provenance (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches focus on retrieval augmentation and focus on the quality of the output . Existing methods focus on generating a highly specific declarative statement ignoring the underlying reasoning process behind ideation. |
| Approach: | They propose a large language model that generates evidence-based hypotheses using literature-guided reasoning and a multi-task setting. |
| Outcome: | The proposed model outperforms the base model and generates evidence-grounded hypotheses with high feasibility and impact as judged by human experts. |