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As LLMs grow more powerful, their most profound achievement may be recognising when to say &quot;I don&#39;t know&quot;. Existing studies on LLM self-knowledge have been largely constrained by human-defined notions of feasibility, often neglecting the reasons behind unanswerability by LLMs and failing to study deficient types of self-knowledge. This study aims to obtain intrinsic insights into different types of LLM self-knowledge with a novel methodology: allowing them the flexibility to set their own feasibility boundaries and then analysing the consistency of these limits. We find that even frontier models like GPT-4o and Mistral Large are not sure of their own capabilities more than 80% of the time, highlighting a significant lack of trustworthiness in responses. Our analysis of confidence balance in LLMs indicates that models swing between overconfidence and conservatism in feasibility boundaries depending on task categories and that the most significant self-knowledge weaknesses lie in temporal awareness and contextual understanding. These difficulties in contextual comprehension additionally lead models to question their operational boundaries, resulting in considerable confusion within the self-knowledge of LLMs. We make our code and results available publicly.

NAACL 2025

Line of Duty: Evaluating LLM Self-Knowledge via Consistency in Feasibility Boundaries

llm self-knowledge

self-knowledge insight

contextual understanding

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
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ACL 2023

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing.

As LLMs grow more powerful, their most profound achievement may be recognising when to say "I don't know". Existing studies on LLM self-knowledge have been largely constrained by human-defined notions of feasibility, often neglecting the reasons behind unanswerability by LLMs and failing to study deficient types of self-knowledge. This study aims to obtain intrinsic insights into different types of LLM self-knowledge with a novel methodology: allowing them the flexibility to set their own feasibility boundaries and then analysing the consistency of these limits. We find that even frontier models like GPT-4o and Mistral Large are not sure of their own capabilities more than 80% of the time, highlighting a significant lack of trustworthiness in responses. Our analysis of confidence balance in LLMs indicates that models swing between overconfidence and conservatism in feasibility boundaries depending on task categories and that the most significant self-knowledge weaknesses lie in temporal awareness and contextual understanding. These difficulties in contextual comprehension additionally lead models to question their operational boundaries, resulting in considerable confusion within the self-knowledge of LLMs. We make our code and results available publicly.

workshop paper

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
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Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Adversarial factuality refers to the deliberate insertion of misinformation into input prompts by an adversary, characterized by varying levels of expressed confidence. In this study, we systematically evaluate the performance of several open-source large language models (LLMs) when exposed to such adversarial inputs. Three tiers of adversarial confidence are considered: strongly confident, moderately confident, and limited confidence. Our analysis encompasses eight LLMs: LLaMA 3.1 (8B), Phi 3 (3.8B), Qwen 2.5 (7B), Deepseek-v2 (16B), Gemma2 (9B), Falcon (7B), Mistrallite (7B), and LLaVA (7B). Empirical results indicate that LLaMA 3.1 (8B) exhibits a robust capability in detecting adversarial inputs, whereas Falcon (7B) shows comparatively lower performance. Notably, for the majority of the models, detection success improves as the adversary's confidence decreases; however, this trend is reversed for LLaMA 3.1 (8B) and Phi 3 (3.8B), where a reduction in adversarial confidence corresponds with diminished detection performance. Further analysis of the queries that elicited the highest and lowest rates of successful attacks reveals that adversarial attacks are more effective when targeting less commonly referenced or obscure information.

Battling Misinformation: An Empirical Study on Adversarial Factuality in Open-Source Large Language Models

Gender bias in pretrained language models (PLMs) poses significant social and ethical challenges. Despite growing awareness, there is a lack of comprehensive investigation into how different models internally represent and propagate such biases. This study adopts an information-theoretic approach to analyze how gender biases are encoded within various encoder-based architectures. We focus on three key aspects: identifying how models encode gender information and biases, examining the impact of bias mitigation techniques and fine-tuning on the encoded biases and their effectiveness, and exploring how model design differences influence the encoding of biases. Through rigorous and systematic investigation, our findings reveal a consistent pattern of gender encoding across diverse models. Surprisingly, debiasing techniques often exhibit limited efficacy, sometimes inadvertently increasing the encoded bias in internal representations while reducing bias in model output distributions. This highlights a disconnect between mitigating bias in output distributions and addressing its internal representations. This work provides valuable guidance for advancing bias mitigation strategies and fostering the development of more equitable language models.

Gender Encoding Patterns in Pretrained Language Model Representations

Continuous prompts, or "soft prompts", are a widely-adopted parameter-efficient tuning strategy for large language models, but are often less favorable due to their opaque nature. Prior attempts to interpret continuous prompts relied on projecting individual prompt tokens onto the vocabulary space. However, this approach is problematic as performant prompts can yield arbitrary or contradictory text, and it individually interprets each prompt token. In this work, we propose a new approach to interpret continuous prompts that elicits textual descriptions from their representations during model inference. Using a Patchscopes variant (Ghandeharioun et al., 2024) called InSPEcT over various tasks, we show our method often yields accurate task descriptions which become more faithful as task performance increases. Moreover, an elaborated version of InSPEcT reveals biased features in continuous prompts, whose presence correlates with biased model predictions. Providing an effective interpretability solution, InSPEcT can be leveraged to debug unwanted properties in continuous prompts and inform developers on ways to mitigate them.

Eliciting Textual Descriptions from Representations of Continuous Prompts

Verifiable generation is introduced to improve the transparency and trustworthiness of outputs produced by large language models (LLMs). Recent studies observe that open-source models struggle to include accurate citations to supporting documents in their generation with in-context learning, in contrast to the strong performance demonstrated by proprietary models. Our work aims to reveal the critical design choices that can benefit open-source models, including generation pipelines, fine-tuning methods, and inference-time compute techniques. We consider three generation pipelines, producing the outputs directly or decomposing the generation into subtasks. These generation pipelines are fine-tuned using supervised fine-tuning and preference-based optimization including further fine-tuning with rejection sampling data and direct preference optimization (DPO). The construction of preference data with varying content and citation diversity is also investigated. Additionally, we examine the benefit of an additional reranking step. With four open-source models, our experiments show that directly generating the outputs achieves the best performance. Compared to other fine-tuning methods, DPO that computes training signals from contrastive pairs consistently yields better performance, and it reaches the peak performance when the contrastive pairs are constructed with sufficient content diversity. We also find that reranking can further boost the performance of verifiable generation systems, but the marginal improvement might not justify the additional cost.

Evaluating Design Choices in Verifiable Generation with Open-source Models

Existing benchmarks are becoming saturated and less effective in evaluating model performance due to factors such as data contamination and the advancing capabilities of the Large Language Models (LLMs). This paper introduces EMDM (Enhanced Model Differentiation Metric), a novel weighted metric designed to revitalize existing benchmarks. EMDM implements a weighting schema for samples based on their complexity and requisite knowledge, utilizing the performance of a baseline LLM in two experimental setups: "Unguided", where the model has no prior exposure to test samples, and "Guided", where the model has prior knowledge about the desired answer. This schema is leveraged in an optimization objective to assign weights to test samples, distinguishing instances of varying complexity. EMDM accounts for both answer correctness and the depth and accuracy of reasoning, offering a more nuanced evaluation of model performance. By weighting test examples based on their required reasoning and knowledge, EMDM achieves a distinguishing range of evaluation scores of 46% among various LLMs, compared to just 17% with traditional exact match (EM) metrics, thereby highlighting the saturation of current evaluation methods.

Revitalizing Saturated Benchmarks: A Weighted Metric Approach for Differentiating Large Language Model Performance

Large Language Models (LLMs) have demonstrated remarkable capability in simulating human behaviors, personality, and language. Such synthetic agents with personalities are considered as cost-effective proxies for real users to facilitate crowd-sourcing efforts like annotations, surveys, and A/B testing. Accordingly, it is imperative to validate knowledge awareness of these LLM persona agents when they are customized for further usage. Currently, there is no established way for such evaluation and appropriate mitigation. In this work, we propose a generic evaluation approach to validate LLM based persona agents for correctness, relevance, and diversity in the context of self-awareness and domain knowledge. We evaluate the efficacy of this framework using three LLMs ( Llama, GPT-4o, and Gemma) for domains such as air travel, gaming, and fitness. We also experiment with advanced prompting strategies such as ReAct and Reflexion. We find that though GPT-4o and Llama demonstrate comparable performance, they fail some of basic consistency checks under certain perturbations.

Know Thyself: Validating Knowledge Awareness of LLM-based Persona Agents

Interpretations of a single sentence can vary, particularly when its context is lost. This paper aims to simulate how readers perceive content with varying toxicity levels by generating diverse interpretations of out-of-context sentences. By modeling toxicity we can anticipate misunderstandings and reveal hidden toxic meanings. Our proposed decoding strategy explicitly controls toxicity in the set of generated interpretations by (i) aligning interpretation toxicity with the input, (ii) relaxing toxicity constraints for more toxic input sentences, and (iii) promoting diversity in toxicity levels within the set of generated interpretations. Experimental results show that our method improves alignment with human-written interpretations in both syntax and semantics while reducing model prediction uncertainty.

Mimicking How Humans Interpret Out-of-Context Sentences Through Controlled Toxicity Decoding

The development of multilingual large language models (LLMs) presents challenges in evaluating their safety across all supported languages. Enhancing safety in one language (e.g., English) may inadvertently introduce vulnerabilities in others. To address this issue, we implement a methodology for the automatic creation of red-teaming datasets for safety evaluation in Polish language. Our approach generates both harmful and non‑harmful prompts by sampling different risk categories and attack styles. We test several open-source models, including those trained on Polish data, and evaluate them using metrics such as Attack Success Rate (ASR) and False Reject Rate (FRR). The results reveal clear gaps in safety performance between models and show that better testing across languages is needed.

Rainbow-Teaming for the Polish Language: A Reproducibility Study

Understanding the inner workings of neural embeddings, particularly in models such as BERT, remains a challenge because of their high-dimensional and opaque nature. This paper proposes a framework for uncovering the specific dimensions of vector embeddings that encode distinct linguistic properties (LPs). We introduce the Linguistically Distinct Sentence Pairs (LDSP-10) dataset, which isolates ten key linguistic features such as synonymy, negation, tense, and quantity. Using this dataset, we analyze BERT embeddings with various methods, including the Wilcoxon signed-rank test, mutual information, and recursive feature elimination, to identify the most influential dimensions for each LP. We introduce a new metric, the Embedding Dimension Impact (EDI) score, which quantifies the relevance of each embedding dimension to a LP. Our findings show that certain properties, such as negation and polarity, are robustly encoded in specific dimensions, while others, like synonymy, exhibit more complex patterns. This study provides insights into the interpretability of embeddings, which can guide the development of more transparent and optimized language models, with implications for model bias mitigation and the responsible deployment of AI systems.

Disentangling Linguistic Features with Dimension-Wise Analysis of Vector Embeddings

This study investigates the effectiveness of the action score, a metric originally developed for computer vision tasks, in estimating sample difficulty across various natural language processing (NLP) tasks. Using transformer-based models, the action score is applied to sentiment analysis, natural language inference, and abstractive text summarization. The results demonstrate that the action score can effectively identify challenging samples in sentiment analysis and natural language inference, often capturing difficult instances that are missed by more established metrics like entropy. However, the effectiveness of the action score appears to be task-dependent, as evidenced by its performance in the abstractive text summarization task, where it exhibits a nearly linear relationship with entropy. The findings suggest that the action score can provide valuable insights into the characteristics of challenging samples in NLP tasks, particularly in classification settings. However, its application should be carefully considered in the context of each specific task and in light of emerging research on the potential value of hard samples in machine learning.

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