Safe and Responsible Large Language Model : Can We Balance Bias Reduction and Language Understanding in Large Language Models?

• URL: http://arxiv.org/abs/2404.01399v3
• Date: Mon, 1 Jul 2024 17:40:13 GMT
• Title: Safe and Responsible Large Language Model : Can We Balance Bias Reduction and Language Understanding in Large Language Models?
• Authors: Shaina Raza, Oluwanifemi Bamgbose, Shardul Ghuge, Fatemeh Tavakol, Deepak John Reji, Syed Raza Bashir,
• Abstract summary: Current approaches to produce unbiased outputs from Large Language Models can reduce biases but at the expense of knowledge retention. We develop the Safety and Responsible Large Language Model (textbfSR$_textLLM$) to diminish biases in generated text. The results confirm that textbfSR$textLLM$ outperforms traditional fine-tuning and prompting methods in both reducing biases and preserving the integrity of language knowledge.
• Score: 2.089112028396727
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Large Language Models (LLMs) have significantly advanced various NLP tasks. However, these models often risk generating unsafe text that perpetuates biases. Current approaches to produce unbiased outputs from LLMs can reduce biases but at the expense of knowledge retention. In this research, we address the question of whether producing safe (unbiased) outputs through LLMs can retain knowledge and language understanding. In response, we developed the Safety and Responsible Large Language Model (\textbf{SR}$_{\text{LLM}}$), an LLM that has been instruction fine-tuned on top of already safe LLMs (e.g., Llama2 or related) to diminish biases in generated text. To achieve our goals, we compiled a specialized dataset designed to train our model in identifying and correcting biased text. We conduct experiments, both on this custom data and out-of-distribution test sets, to show the bias reduction and knowledge retention. The results confirm that \textbf{SR}$_{\text{LLM}}$ outperforms traditional fine-tuning and prompting methods in both reducing biases and preserving the integrity of language knowledge. The significance of our findings lies in demonstrating that instruction fine-tuning can provide a more robust solution for bias reduction in LLMs. We have made our code and data available at \href{https://github.com/shainarazavi/Safe-Responsible-LLM}{Safe-LLM}.

Related papers

• Course-Correction: Safety Alignment Using Synthetic Preferences [17.897817682322053]
  We introduce the textscC$2$-Eval benchmark for quantitative assessment and analyze 10 popular language models. Using an automated pipeline, we create textscC$2$-Syn, a synthetic dataset with 750K pairwise preferences. Experiments on 2 LLMs, textscLlama2-Chat 7B and textscQwen2 7B, show that our method effectively enhances course-correction skills without affecting general performance.
  arXiv  Detail & Related papers  (2024-07-23T16:54:28Z)
• $\forall$uto$\exists$val: Autonomous Assessment of LLMs in Formal Synthesis and Interpretation Tasks [21.12437562185667]
  This paper presents a new approach for scaling LLM assessment in translating formal syntax to natural language. We use context-free grammars (CFGs) to generate out-of-distribution datasets on the fly. We also conduct an assessment of several SOTA closed and open-source LLMs to showcase the feasibility and scalability of this paradigm.
  arXiv  Detail & Related papers  (2024-03-27T08:08:00Z)
• TEaR: Improving LLM-based Machine Translation with Systematic Self-Refinement [26.26493253161022]
  Large Language Models (LLMs) have achieved impressive results in Machine Translation (MT) We introduce a systematic LLM-based self-refinement translation framework, named textbfTEaR.
  arXiv  Detail & Related papers  (2024-02-26T07:58:12Z)
• Pride and Prejudice: LLM Amplifies Self-Bias in Self-Refinement [75.7148545929689]
  Large language models (LLMs) improve their performance through self-feedback on certain tasks while degrade on others. We formally define LLM's self-bias - the tendency to favor its own generation. We analyze six LLMs on translation, constrained text generation, and mathematical reasoning tasks.
  arXiv  Detail & Related papers  (2024-02-18T03:10:39Z)
• Supervised Knowledge Makes Large Language Models Better In-context Learners [94.89301696512776]
  Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering. The challenge of improving the generalizability and factuality of LLMs in natural language understanding and question answering remains under-explored. We propose a framework that enhances the reliability of LLMs as it: 1) generalizes out-of-distribution data, 2) elucidates how LLMs benefit from discriminative models, and 3) minimizes hallucinations in generative tasks.
  arXiv  Detail & Related papers  (2023-12-26T07:24:46Z)
• Gaining Wisdom from Setbacks: Aligning Large Language Models via Mistake Analysis [127.85293480405082]
  The rapid development of large language models (LLMs) has not only provided numerous opportunities but also presented significant challenges. Existing alignment methods usually direct LLMs toward the favorable outcomes by utilizing human-annotated, flawless instruction-response pairs. This study proposes a novel alignment technique based on mistake analysis, which deliberately exposes LLMs to erroneous content to learn the reasons for mistakes and how to avoid them.
  arXiv  Detail & Related papers  (2023-10-16T14:59:10Z)
• On the Safety of Open-Sourced Large Language Models: Does Alignment Really Prevent Them From Being Misused? [49.99955642001019]
  We show that open-sourced, aligned large language models could be easily misguided to generate undesired content. Our key idea is to directly manipulate the generation process of open-sourced LLMs to misguide it to generate undesired content.
  arXiv  Detail & Related papers  (2023-10-02T19:22:01Z)
• Are Large Language Models Really Robust to Word-Level Perturbations? [68.60618778027694]
  We propose a novel rational evaluation approach that leverages pre-trained reward models as diagnostic tools. Longer conversations manifest the comprehensive grasp of language models in terms of their proficiency in understanding questions. Our results demonstrate that LLMs frequently exhibit vulnerability to word-level perturbations that are commonplace in daily language usage.
  arXiv  Detail & Related papers  (2023-09-20T09:23:46Z)
• Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMs [59.596335292426105]
  This paper collects the first open-source dataset to evaluate safeguards in large language models. We train several BERT-like classifiers to achieve results comparable with GPT-4 on automatic safety evaluation.
  arXiv  Detail & Related papers  (2023-08-25T14:02:12Z)
• Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models [37.63939774027709]
  Large language models (LLMs) specializing in natural language generation (NLG) have recently started exhibiting promising capabilities. We propose and compare several confidence/uncertainty measures, applying them to *selective NLG* where unreliable results could either be ignored or yielded for further assessment. Results reveal that a simple measure for the semantic dispersion can be a reliable predictor of the quality of LLM responses.
  arXiv  Detail & Related papers  (2023-05-30T16:31:26Z)

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.