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Towards Trustworthy Large Language Models

Sakshi Parate Shreyans Sanyal
Aug 2026 · Advanced International Journal for Research · 0 citations · 9 references

TL;DR

An integrated conceptual frame-work that couples attention- and perturbation-based explainability with lightweight hallucination-detection signals and token-efficient inference strategies is presented, and a set of cross-cutting consistency metrics are instrumented with a set of cross-cutting consistency metrics.

Abstract

Large language models (LLMs) are increasingly deployed in domains where opacity, factual un-reliability, and computational cost carry real consequences, yet the research communities that address these three problems — explainability, hallucination, and inference efficiency — have largely developed in isolation. This paper argues that the three concerns are not independent: interventions designed to reduce inference cost, such as prompt compression, can silently alter both the faithfulness of post-hoc explanations and the model’s propensity to hallucinate. We present an integrated conceptual frame-work that couples attention- and perturbation-based explainability (LIME, SHAP, raw attention) with lightweight hallucination-detection signals and token-efficient inference strategies (chunking, summa-rization, prompt compression), and we instrument the framework with a set of cross-cutting consistency metrics. To ground the framework empirically, we design and execute a controlled pilot experiment that measures how rule-based prompt compression jointly affects (i) the Kullback–Leibler divergence between a model’s output distributions before and after compression, used as a proxy for hallucination risk, and (ii) the entropy and rank-correlation of last-layer attention, used as a proxy for explanation faithfulness. Because the sandboxed experimental environment used for this study has no network ac-cess to pretrained model repositories, the experiment is conducted on a compact two-layer Transformer language model trained from scratch on a controlled synthetic corpus, which allows exact, reproducible control over ground truth while preserving the qualitative mechanics of attention-based attribution and next-token prediction under compression. Across 15 held-out prompts, compression removes 52.9% of tokens on average while producing a small but non-zero mean output KL divergence of 0.0007 and re-ducing mean attention entropy from 1.062 to 0.632, with attention rank correlation across compression conditions of only 0.633. These results provide direct, quantitative evidence for the framework’s central hypothesis: token-efficiency interventions are not explanation-neutral, and systems that report compres-sion ratios without also reporting faithfulness and hallucination-risk deltas may be masking a three-way trade-off. We discuss the implications for trustworthy LLM system design and outline how the pilot findings motivate follow-up experiments on larger pretrained models.

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