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Unmasking Hallucinations: A Causal Graph-Attention Perspective on Factual Reliability in Large Language Models

arXiv:2604.04020v1 Announce Type: new Abstract: This paper primarily focuses on the hallucinations caused due to AI language models(LLMs).LLMs have shown extraordinary Language understanding and generation capabilities .Still it has major a disadvantage hallucinations which give outputs which are factually incorrect ,misleading or unsupported by input data . These hallucinations cause serious problems in scenarios like medical diagnosis or legal reasoning.Through this work,we propose causal graph attention network (GCAN) framework that reduces hallucinations through interpretation of internal attention flow within a transformer architecture with the help of constructing token level graphs that combine self attention weights and gradient based influence scores.our method quantifies each tokens factual dependency using a new metric called the Causal Contribution Score (CCS). We further introduce a fact-anchored graph reweighting layer that dynamically reduces the influence of hallucinat

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Sailesh kiran kurra, Shiek Ruksana, Vishal Borusu · · 1 min read · 8 views
#cs.CL #cs.LG

arXiv:2604.04020v1 Announce Type: new Abstract: This paper primarily focuses on the hallucinations caused due to AI language models(LLMs).LLMs have shown extraordinary Language understanding and generation capabilities .Still it has major a disadvantage hallucinations which give outputs which are factually incorrect ,misleading or unsupported by input data . These hallucinations cause serious problems in scenarios like medical diagnosis or legal reasoning.Through this work,we propose causal graph attention network (GCAN) framework that reduces hallucinations through interpretation of internal attention flow within a transformer architecture with the help of constructing token level graphs that combine self attention weights and gradient based influence scores.our method quantifies each tokens factual dependency using a new metric called the Causal Contribution Score (CCS). We further introduce a fact-anchored graph reweighting layer that dynamically reduces the influence of hallucination prone nodes during generation. Experiments on standard benchmarks such as TruthfulQA and HotpotQA show a 27.8 percent reduction in hallucination rate and 16.4 percent improvement in factual accuracy over baseline retrieval-augmented generation (RAG) models. This work contributes to the interpretability,robustness, and factual reliability of future LLM architectures.

Executive Summary

This study addresses the issue of hallucinations in large language models (LLMs), where models generate factually incorrect, misleading, or unsupported outputs. The authors propose a causal graph attention network (GCAN) framework that interprets internal attention flow within a transformer architecture, constructing token-level graphs and introducing a fact-anchored graph reweighting layer to dynamically reduce hallucination-prone nodes. The method is evaluated on standard benchmarks, achieving a 27.8% reduction in hallucination rate and 16.4% improvement in factual accuracy. The study contributes to the interpretability, robustness, and factual reliability of future LLM architectures, with potential applications in high-stakes scenarios such as medical diagnosis and legal reasoning.

Key Points

  • ▸ The study highlights the issue of hallucinations in large language models and proposes a novel approach to address it.
  • ▸ The authors introduce a causal graph attention network (GCAN) framework to reduce hallucinations in transformer-based architectures.
  • ▸ The method is evaluated on standard benchmarks, achieving significant reductions in hallucination rate and improvements in factual accuracy.

Merits

Strengths in Methodological Approach

The study's use of causal graph attention networks and fact-anchored graph reweighting layers represents a novel and innovative approach to addressing hallucinations in LLMs.

Significant Improvements in Performance

The study achieves substantial reductions in hallucination rate and improvements in factual accuracy, demonstrating the efficacy of the proposed method.

Demerits

Limitation in Generalizability

The study's findings may not generalize to all LLM architectures or domains, requiring further investigation to confirm the method's effectiveness.

Computational Resource Intensiveness

The GCAN framework and fact-anchored graph reweighting layer may require significant computational resources, potentially limiting the method's practical applications.

Expert Commentary

This study represents a significant contribution to the field of natural language processing, addressing a critical issue in LLMs. The proposed GCAN framework and fact-anchored graph reweighting layer demonstrate a novel approach to reducing hallucinations and improving factual accuracy. While the study's findings are promising, further research is needed to confirm the method's generalizability and scalability. The study's implications for the development of more robust and factually reliable LLMs are substantial, and its results will likely influence the direction of future research in this area.

Recommendations

  • ✓ Future research should investigate the applicability of the GCAN framework and fact-anchored graph reweighting layer to other LLM architectures and domains.
  • ✓ The study's findings should be replicated and extended to confirm the method's efficacy and generalizability.

Sources

Original: arXiv - cs.CL

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