A Trip Towards Fairness: Bias and De-Biasing in Large Language Models (2024.starsem-1)
Copied to clipboard
| 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. |
Similar Papers
A Generative Approach for Mitigating Structural Biases in Natural Language Inference (2022.starsem-1)
Copied to clipboard
| Challenge: | Natural language inference datasets contain artifacts and biases that allow models to perform poorly by using a biased subset of the input without considering the remainder features. |
| Approach: | They reformulate a natural language inference task as a generative task . they find that this approach is highly robust to large amounts of bias . |
| Outcome: | The proposed model is highly robust to large amounts of bias. |
LEXPLAIN: Improving Model Explanations via Lexicon Supervision (2023.starsem-1)
Copied to clipboard
| Challenge: | Existing methods that extract features from input text to explain a classifier's prediction are limiting to models that are faithful to their predictions. |
| Approach: | They propose a framework for guiding model explanations by supervising them explicitly using task-related lexicons to direct supervise model explanation. |
| Outcome: | The proposed method improves model explanations without sacrificing performance on sentiment analysis and toxicity detection tasks while demoting spurious correlations with African American English dialects. |
Language models are not naysayers: an analysis of language models on negation benchmarks (2023.starsem-1)
Copied to clipboard
| Challenge: | Negation has been shown to be a major bottleneck for masked language models, such as BERT, but whether this finding still holds for larger-sized auto-regressive language models has not been studied comprehensively. |
| Approach: | They evaluate the ability of current-generation auto-regressive language models to handle negation using a wide range of benchmarks and models. |
| Outcome: | The proposed models are compared against a wide range of negation benchmarks and show that they are insensitive to negation, inability to capture the lexical semantics of negations, and failure to reason under negation. |
Natural Language Inference with Mixed Effects (2020.starsem-1)
Copied to clipboard
| Challenge: | aggregating raw annotations to a single label is problematic due to disagreement among annotators. |
| Approach: | They propose a generic method that allows one to skip the aggregation step and train on the raw annotations directly without subjecting the model to unwanted noise. |
| Outcome: | The proposed method improves performance over models that do not incorporate such effects. |
Exploring Factual Entailment with NLI: A News Media Study (2024.starsem-1)
Copied to clipboard
| Challenge: | Recent studies have focused on the relationship between factuality and Natural Language Inference (NLI). |
| Approach: | They propose a novel annotation scheme that models factual rather than textual entailment and use it to annotate a dataset of naturally occurring sentences from news articles. |
| Outcome: | The proposed annotation scheme can be used to model factual relationships on a dataset of naturally occurring sentences from news articles. |
Syntax and Semantics Meet in the “Middle”: Probing the Syntax-Semantics Interface of LMs Through Agentivity (2023.starsem-1)
Copied to clipboard
| Challenge: | a recent study examined how large language models handle interactions in meaning across words and larger syntactic forms. |
| Approach: | They propose to use a dataset to examine the linguistic properties of optionally transitive English verbs to examine their agentivity. |
| Outcome: | The proposed model outperforms all other models in the evaluation dataset . the results are better correlated with human judgements than syntactic and semantic corpus statistics . |
Limits for learning with language models (2023.starsem-1)
Copied to clipboard
| Challenge: | Recent studies show that large language models fail to capture important aspects of linguistic meaning . authors argue that LLMs cannot learn fundamental semantic properties defined in formal semantics . |
| Approach: | They propose a theoretical explanation for some of the observed failings of large language models . they show that LLMs cannot learn certain fundamental semantic properties . |
| Outcome: | The proposed model fails to learn semantic entailment and consistency as defined in formal semantics, the authors argue . their model fails on tasks that require engorgements and deep linguistic understanding, they argue - but not on universal quantification. |
How Does Stereotype Content Differ across Data Sources? (2024.starsem-1)
Copied to clipboard
| Challenge: | Existing studies of stereotypes using rating scales capture beliefs and opinions about different social groups. |
| Approach: | They compare stereotype-relevant measures of social group social status with traditional scales and a word-list generation task using free-text data. |
| Outcome: | The results compare with traditional surveys and a spontaneous word-list generation task. |
How Does Data Corruption Affect Natural Language Understanding Models? A Study on GLUE datasets (2022.starsem-1)
Copied to clipboard
| Challenge: | Existing studies on the performance of pre-trained language models on natural language understanding tasks have focused on the natural language inference and textual entailment tasks. |
| Approach: | They propose to use corrupted data to fine-tune pre-trained language models to assess their language understanding capabilities. |
| Outcome: | The proposed transformations can be applied to all but one NLU task and show that understanding the meaning of utterances is not required for high performance. |
Analyzing Syntactic Generalization Capacity of Pre-trained Language Models on Japanese Honorific Conversion (2023.starsem-1)
Copied to clipboard
| Challenge: | Using Japanese honorifics requires knowledge of grammatical rules and contextual information, such as social relationships. |
| Approach: | They propose a Japanese honorific conversion task that considers social relationships among people mentioned in a conversation. |
| Outcome: | The proposed model performed better on the context-aware task than the prompt-based one. |