Social Bias Evaluation for Large Language Models Requires Prompt Variations (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Recent studies have tried to evaluate and mitigate social biases accurately using limited prompts. |
| Approach: | They investigate the sensitivity of Large Language Models when changing prompt variations . they found that LLM rankings fluctuate across prompts for both task performance and social bias . |
| Outcome: | The results show that LLM rankings fluctuate when changing prompt variations . |
Similar Papers
Characterizing Positional Bias in Large Language Models: A Multi-Model Evaluation of Prompt Order Effects (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models can be influenced by various forms of biases, says a new study . positional bias affects how LLMs interpret and weigh information, the authors say . |
| Approach: | a new study examines the impact of positional bias on large language models . positional biased models prioritize items based on their position rather than content or quality . |
| Outcome: | a new study shows that LLMs prioritize items based on their position rather than content or quality . the positional bias affects how LLM interpret and weigh information, the authors say . |
Rethinking Prompt-based Debiasing in Large Language Model (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing prompt-based methods for debiasing are often superficial and lack a thorough understanding of complex bias concepts. |
| Approach: | They analyze a BBQ and stereoSet benchmarks to examine the assumption that large language models understand biases. |
| Outcome: | The proposed model misclassified 90% of unbiased content as biased despite high accuracy on BBQ dataset . the proposed model may have been flawed in previous attempts to debiase . |
Beyond Performance: Quantifying and Mitigating Label Bias in LLMs (2024.naacl-long)
Copied to clipboard
| Challenge: | Large language models exhibit undesirable preference toward predicting certain answers over others, despite their adaptability to diverse tasks. |
| Approach: | They propose a label bias calibration method that outperforms recent calibration approaches for improving performance and mitigating label bias. |
| Outcome: | The proposed method outperforms calibration approaches for improving performance and mitigating label bias. |
Flaw or Artifact? Rethinking Prompt Sensitivity in Evaluating LLMs (2025.emnlp-main)
Copied to clipboard
| Challenge: | a high prompt sensitivity has been widely accepted as a core limitation of large language models . a recent study suggests that prompt senescence may be an artifact of evaluation processes . |
| Approach: | They examine whether prompt sensitivity is an inherent weakness or an artifact of evaluation . they find that heuristic evaluation methods overlook semantically correct responses . large language models have achieved remarkable success across a wide range of tasks . |
| Outcome: | The proposed model is more robust to prompt templates than previously thought . the authors show that prompt sensitivity may be an artifact of evaluation rather than a flaw . |
Understanding Large Language Model Vulnerabilities to Social Bias Attacks (2025.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable linguistic capabilities across tasks . however, there is a growing concern about their potential to perpetuate social biases . |
| Approach: | They evaluate LLMs across gender, racial, and religious bias types . they also explore cross-bias and multiple-biases attacks . |
| Outcome: | The proposed models are more susceptible to gender bias attacks than racial or religious biases. |
On Measuring Social Biases in Prompt-Based Multi-Task Learning (2022.findings-naacl)
Copied to clipboard
| Challenge: | a large body of work within prompt engineering attempts to understand the effects of input forms and prompts in achieving superior performance. |
| Approach: | They propose a large-scale text-to-text language model trained using prompts . they consider two different forms of semantically equivalent inputs - question-answer format and premise-hypothesis format . |
| Outcome: | The proposed model can generalize into novel forms of language and handle novel tasks. |
Unveiling Selection Biases: Exploring Order and Token Sensitivity in Large Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | Using zero-shot or few-shot prompting, Large Language Models have been widely adopted in downstream applications. |
| Approach: | They propose to quantify the impact of option order and token usage on LLMs and propose mitigation strategies to enhance model performance. |
| Outcome: | The proposed mitigation strategies improve model performance and reduce the impact of token and order sensitivity on LLMs. |
POSIX: A Prompt Sensitivity Index For Large Language Models (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are sensitive to minor variations in prompts, such as spelling errors, alteration of wording or the prompt template. |
| Approach: | They propose a PrOmpt Sensitivity IndeX to measure prompt sensitivity . they use this to compare prompt sensitability of various open source LLMs . |
| Outcome: | The proposed method can measure and compare prompt sensitivity of open source LLMs. |
What Did I Do Wrong? Quantifying LLMs’ Sensitivity and Consistency to Prompt Engineering (2025.naacl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have significantly improved productivity in a number of routine tasks. |
| Approach: | They propose two metrics for classification tasks, namely *sensitivity* and *consistency*, which are complementary to task performance. |
| Outcome: | The proposed metrics are complementary to task performance and can be used to guide prompt engineering and obtain LLMs that balance robustness and performance. |
Do LLMs Overcome Shortcut Learning? An Evaluation of Shortcut Challenges in Large Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have shown remarkable capabilities in various tasks, but may rely on dataset biases as shortcuts for prediction. |
| Approach: | They propose to use a test suite to evaluate the impact of shortcuts on LLMs' performance. |
| Outcome: | The proposed test suite incorporates six shortcut types, five evaluation metrics, and four prompting strategies. |