Papers with Large-Language Models
A Trip Towards Fairness: Bias and De-Biasing in Large Language Models (2024.starsem-1)
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| Challenge: | a little or a large bias in CtB-LLMs may cause huge harm . LLaMA and OPT families have an important bias in gender, race, religion, and profession. |
| Approach: | They propose to debiase three families of Very Large-Language Models with LORA to reduce bias by 4.12 points in the normalized stereotype score. |
| Outcome: | The proposed model reduces bias up to 4.12 points in the normalized stereotype score. |
Navigating the Nuances: A Fine-grained Evaluation of Vision-Language Navigation (2024.findings-emnlp)
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| Challenge: | a new evaluation framework for vision-language navigation is proposed . current evaluation standards hinge on endpoint success rates and path alignment metrics . |
| Approach: | They propose a semi-automatic method for CFG construction with Large-Language Models . they induct data spanning five principal instruction categories and analyze them . |
| Outcome: | The proposed framework diagnoses current models for the Vision-Language Navigation task at a finer-grained level. |
AssertionBench: A Benchmark to Evaluate Large-Language Models for Assertion Generation (2025.findings-naacl)
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| Challenge: | Assertions have been the de facto collateral for hardware for over a decade. |
| Approach: | They propose a benchmark to evaluate LLMs’ effectiveness for assertion generation quantitatively. |
| Outcome: | The proposed benchmark compares state-of-the-art LLMs with existing benchmarks and shows that they generate higher fractions of functionally correct assertions. |
Who Wrote it and Why? Prompting Large-Language Models for Authorship Verification (2023.findings-emnlp)
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| Challenge: | Existing AV techniques, including stylometric and deep learning, face limitations in terms of data requirements and lack of explainability. |
| Approach: | They propose a technique that leverages Large-Language Models (LLMs) to provide step-by-step stylometric explanation prompts to verify authorship. |
| Outcome: | The proposed technique outperforms state-of-the-art baselines, operates effectively with limited training data, and enhances interpretability through intuitive explanations. |
Rubrik’s Cube: Testing a New Rubric for Evaluating Explanations on the CUBE dataset (2025.acl-long)
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Diana Galvan-Sosa, Gabrielle Gaudeau, Pride Kavumba, Yunmeng Li, Hongyi Gu, Zheng Yuan, Keisuke Sakaguchi, Paula Buttery
| Challenge: | Large-Language Models (LLMs) are increasingly being used in explanation generation tasks due to their unreliability. |
| Approach: | They propose a rubric and a dataset of 26k explanations written and quality-annotated using the rubric by humans and six open- and closed-source LLMs to test their proposed rubric. |
| Outcome: | The proposed rubric and CUBE dataset focuses on reasoning and language tasks and provides the necessary diversity to test it. |
Bypass Back-propagation: Optimization-based Structural Pruning for Large Language Models via Policy Gradient (2025.acl-long)
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| Challenge: | Recent pruning methods rely on heuristically hand-crafted metrics, leading to suboptimal performance. |
| Approach: | They propose a method that optimizes pruning masks by minimizing back-propagation . they learn an underlying Bernoulli distribution to sample binary pruning mask samples . |
| Outcome: | The proposed method is able to support global and heterogeneous pruning without back-propagation. |