ymoslem/acl-abstracts huggingface dataset:
Can a Prediction`s Rank Offer a More Accurate Quantification of Bias? A Case Study Measuring Sexism in Debiased Language Models (2023)

Pre-trained language models are known to inherit a plethora of contextual biases from their training data. These biases have proven to be projected onto a variety of downstream applications, making their detection and mitigation imminent. Limited research has been conducted to quantify specific bias types, such as benevolent sexism, which may be subtly present within the inferred connotations of a sentence. To this extent, our work aims to: (1) provide a benchmark of sexism sentences; (2) adapt two bias metrics: mean probability score and mean normalized rank; (3) conduct a case study to quantify and analyze sexism in base and de-biased masked language models. We find that debiasing, even in its most effective form (Auto-Debias), solely nullifies the probability score of biasing tokens, while retaining them in high ranks. Auto-Debias illustrates a 90{\%}-96{\%} reduction in mean probability scores from base to debiased models, while only a 3{\%}-16{\%} reduction in mean normalized ranks. Similar to the application of non-parametric statistical tests for data that does not follow a normal distribution, operating on the ranks of predictions rather than their probability scores offers a more representative bias measure.
The Eval4NLP 2023 Shared Task on Prompting Large Language Models as Explainable Metrics (2023)

Generative large language models (LLMs) have seen many breakthroughs over the last year. With an increasing number of parameters and pre-training data, they have shown remarkable capabilities to solve tasks with minimal or no task-related examples. Notably, LLMs have been successfully employed as evaluation metrics in text generation tasks. Strategies employed in this context differ in the choice of input prompts, the selection of samples for demonstration, and the methodology used to construct scores grading the generations. Approaches often differ in the input prompts, the samples that are selected for demonstration and the construction process of scores from the output. Within this context, we introduce the Eval4NLP 2023 shared task that asks participants to explore such approaches for machine translation evaluation and summarization eval- uation. Specifically, we select a list of allowed LLMs and disallow fine-tuning to ensure a focus on prompting. We test the approaches of the participants on a new reference-free test-set spanning 3 language pairs for machine transla- tion as well as a summarization dataset. Further, we present an overview of the approaches taken by the participants, present their results on the test set and analyze paths for future work. Fi- nally, as a separate track, we perform a human evaluation of the plausibility of explanations given by the LLMs and its effect on model performance. We make parts of our code and datasets available.
HIT-MI\&T Lab`s Submission to Eval4NLP 2023 Shared Task (2023)

Recently, Large Language Models (LLMs) have boosted the research in natural language processing and shown impressive capabilities across numerous domains, including machine translation evaluation. This paper presents our methods developed for the machine translation evaluation sub-task of the Eval4NLP 2023 Shared Task. Based on the provided LLMs, we propose a generation-based method as well as a probability-based method to perform evaluation, explore different strategies when selecting the demonstrations for in-context learning, and try different ensemble methods to further improve the evaluation accuracy. The experiment results on the development set and test set demonstrate the effectiveness of our proposed method.
Understanding Large Language Model Based Metrics for Text Summarization (2023)

This paper compares the two most widely used techniques for evaluating generative tasks with large language models (LLMs): prompt-based evaluation and log-likelihood evaluation as part of the Eval4NLP shared task. We focus on the summarization task and evaluate both small and large LLM models. We also study the impact of LLAMA and LLAMA 2 on summarization, using the same set of prompts and techniques. We used the Eval4NLP dataset for our comparison. This study provides evidence of the advantages of prompt-based evaluation techniques over log-likelihood based techniques, especially for large models and models with better reasoning power.
LTRC\_IIITH`s 2023 Submission for Prompting Large Language Models as Explainable Metrics Task (2023)

In this report, we share our contribution to the Eval4NLP Shared Task titled {\textquotedblleft}Prompting Large Language Models as Explainable Metrics.{\textquotedblright} We build our prompts with a primary focus on effective prompting strategies, score-aggregation, and explainability for LLM-based metrics. We participated in the track for smaller models by submitting the scores along with their explanations. According to the Kendall correlation scores on the leaderboard, our MT evaluation submission ranks second-best, while our summarization evaluation submission ranks fourth, with only a 0.06 difference from the leading submission.
Which is better? Exploring Prompting Strategy For LLM-based Metrics (2023)

This paper describes the DSBA submissions to the Prompting Large Language Models as Explainable Metrics shared task, where systems were submitted to two tracks: small and large summarization tracks. With advanced Large Language Models (LLMs) such as GPT-4, evaluating the quality of Natural Language Generation (NLG) has become increasingly paramount. Traditional similarity-based metrics such as BLEU and ROUGE have shown to misalign with human evaluation and are ill-suited for open-ended generation tasks. To address this issue, we explore the potential capability of LLM-based metrics, especially leveraging open-source LLMs. In this study, wide range of prompts and prompting techniques are systematically analyzed with three approaches: prompting strategy, score aggregation, and explainability. Our research focuses on formulating effective prompt templates, determining the granularity of NLG quality scores and assessing the impact of in-context examples on LLM-based evaluation. Furthermore, three aggregation strategies are compared to identify the most reliable method for aggregating NLG quality scores. To examine explainability, we devise a strategy that generates rationales for the scores and analyzes the characteristics of the explanation produced by the open-source LLMs. Extensive experiments provide insights regarding evaluation capabilities of open-source LLMs and suggest effective prompting strategies.
Characterised LLMs Affect its Evaluation of Summary and Translation (2023)

In today`s widespread use of Large Language Models (LLMs), there have been significant achievements in various text domains such as generating summaries and translations. However, there is still room for development and improvement in evaluating the outputs of LLMs. In this paper, we propose an innovative scoring system that assesses the quality of summaries and translations using multiple metrics, we also enhance LLM`s performance in scoring tasks by assigning it different roles, effectively making it act as an expert. We test four roles in the study: a teacher, a proofreader, a travel writer, and an internet troll, comparing the advantages and disadvantages of each role in the scoring task. Our research results demonstrate that emphasizing LLM`s multilingual capabilities and strict standards as its identity can effectively boost its performance. Additionally, imbuing LLM with a more critical thinking ability enhances its performance in translation tasks compared to a milder LLM identity. In summary, we show that assigning different identities to LLM can influence its performance in scoring tasks. We believe that this research will contribute to the use of LLMs for scoring purposes.
Reference-Free Summarization Evaluation with Large Language Models (2023)

With the continuous advancement in unsupervised learning methodologies, text generation has become increasingly pervasive. However, the evaluation of the quality of the generated text remains challenging. Human annotations are expensive and often show high levels of disagreement, in particular for certain tasks characterized by inherent subjectivity, such as translation and summarization.Consequently, the demand for automated metrics that can reliably assess the quality of such generative systems and their outputs has grown more pronounced than ever. In 2023, Eval4NLP organized a shared task dedicated to the automatic evaluation of outputs from two specific categories of generative systems: machine translation and summarization. This evaluation was achieved through the utilization of prompts with Large Language Models. Participating in the summarization evaluation track, we propose an approach that involves prompting LLMs to evaluate six different latent dimensions of summarization quality. In contrast to many previous approaches to summarization assessments, which emphasize lexical overlap with reference text, this method surfaces the importance of correct syntax in summarization evaluation. Our method resulted in the second-highest performance in this shared task, demonstrating its effectiveness as a reference-free evaluation.
Little Giants: Exploring the Potential of Small LLMs as Evaluation Metrics in Summarization in the Eval4NLP 2023 Shared Task (2023)

This paper describes and analyzes our participation in the 2023 Eval4NLP shared task, which focuses on assessing the effectiveness of prompt-based techniques to empower Large Language Models to handle the task of quality estimation, particularly in the context of evaluating machine translations and summaries. We conducted systematic experiments with various prompting techniques, including standard prompting, prompts informed by annotator instructions, and innovative chain-of-thought prompting. In addition, we integrated these approaches with zero-shot and one-shot learning methods to maximize the efficacy of our evaluation procedures. Our work reveals that combining these approaches using a {\textquotedblleft}small{\textquotedblright}, open source model (orca{\_}mini{\_}v3{\_}7B) yields competitive results.
Exploring Prompting Large Language Models as Explainable Metrics (2023)

This paper describes the IUST NLP Lab submission to the Prompting Large Language Models as Explainable Metrics Shared Task at the Eval4NLP 2023 Workshop on Evaluation {\&} Comparison of NLP Systems. We have proposed a zero-shot prompt-based strategy for explainable evaluation of the summarization task using Large Language Models (LLMs). The conducted experiments demonstrate the promising potential of LLMs as evaluation metrics in Natural Language Processing (NLP), particularly in the field of summarization. Both few-shot and zero-shot approaches are employed in these experiments. The performance of our best provided prompts achieved a Kendall correlation of 0.477 with human evaluations in the text summarization task on the test data.
Team NLLG submission for Eval4NLP 2023 Shared Task: Retrieval-Augmented In-Context Learning for NLG Evaluation (2023)

In this paper, we propose a retrieval-augmented in-context learning for natural language generation (NLG) evaluation. This method allows practitioners to utilize large language models (LLMs) for various NLG evaluation tasks without any fine-tuning. We apply our approach to Eval4NLP 2023 Shared Task in translation evaluation and summarization evaluation subtasks. The findings suggest that retrieval-augmented in-context learning is a promising approach for creating LLM-based evaluation metrics for NLG. Further research directions include exploring the performance of various publicly available LLM models and identifying which LLM properties help boost the quality of the metric.
IAG: Induction-Augmented Generation Framework for Answering Reasoning Questions (2023)

Retrieval-Augmented Generation (RAG), by incorporating external knowledge with parametric memory of language models, has become the state-of-the-art architecture for open-domain QA tasks. However, common knowledge bases are inherently constrained by limited coverage and noisy information, making retrieval-based approaches inadequate to answer implicit reasoning questions. In this paper, we propose an Induction-Augmented Generation (IAG) framework that utilizes inductive knowledge along with the retrieved documents for implicit reasoning. We leverage large language models (LLMs) for deriving such knowledge via a novel prompting method based on inductive reasoning patterns. On top of this, we implement two versions of IAG named IAG-GPT and IAG-Student, respectively. IAG-GPT directly utilizes the knowledge generated by GPT-3 for answer prediction, while IAG-Student gets rid of dependencies on GPT service at inference time by incorporating a student inductor model. The inductor is firstly trained via knowledge distillation and further optimized by back-propagating the generator feedback via differentiable beam scores. Experimental results show that IAG outperforms RAG baselines as well as ChatGPT on two Open-Domain QA tasks. Notably, our best models have won the first place in the official leaderboards of CSQA2.0 (since Nov 1, 2022) and StrategyQA (since Jan 8, 2023).
Absolute Position Embedding Learns Sinusoid-like Waves for Attention Based on Relative Position (2023)

Attention weight is a clue to interpret how a Transformer-based model makes an inference. In some attention heads, the attention focuses on the neighbors of each token. This allows the output vector of each token to depend on the surrounding tokens and contributes to make the inference context-dependent. We analyze the mechanism behind the concentration of attention on nearby tokens. We show that the phenomenon emerges as follows: (1) learned position embedding has sinusoid-like components, (2) such components are transmitted to the query and the key in the self-attention, (3) the attention head shifts the phases of the sinusoid-like components so that the attention concentrates on nearby tokens at specific relative positions. In other words, a certain type of Transformer-based model acquires the sinusoidal positional encoding to some extent on its own through Masked Language Modeling.
Chinese Lexical Substitution: Dataset and Method (2023)

Existing lexical substitution (LS) benchmarks were collected by asking human annotators to think of substitutes from memory, resulting in benchmarks with limited coverage and relatively small scales. To overcome this problem, we propose a novel annotation method to construct an LS dataset based on human and machine collaboration. Based on our annotation method, we construct the first Chinese LS dataset CHNLS which consists of 33,695 instances and 144,708 substitutes, covering three text genres (News, Novel, and Wikipedia). Specifically, we first combine four unsupervised LS methods as an ensemble method to generate the candidate substitutes, and then let human annotators judge these candidates or add new ones. This collaborative process combines the diversity of machine-generated substitutes with the expertise of human annotators. Experimental results that the ensemble method outperforms other LS methods. To our best knowledge, this is the first study for the Chinese LS task.
Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecasting (2023)

Automatic response forecasting for news media plays a crucial role in enabling content producers to efficiently predict the impact of news releases and prevent unexpected negative outcomes such as social conflict and moral injury. To effectively forecast responses, it is essential to develop measures that leverage the social dynamics and contextual information surrounding individuals, especially in cases where explicit profiles or historical actions of the users are limited (referred to as lurkers). As shown in a previous study, 97{\%} of all tweets are produced by only the most active 25{\%} of users. However, existing approaches have limited exploration of how to best process and utilize these important features. To address this gap, we propose a novel framework, named SocialSense, that leverages a large language model to induce a belief-centered graph on top of an existent social network, along with graph-based propagation to capture social dynamics. We hypothesize that the induced graph that bridges the gap between distant users who share similar beliefs allows the model to effectively capture the response patterns. Our method surpasses existing state-of-the-art in experimental evaluations for both zero-shot and supervised settings, demonstrating its effectiveness in response forecasting. Moreover, the analysis reveals the framework`s capability to effectively handle unseen user and lurker scenarios, further highlighting its robustness and practical applicability.
Fine-grained Conversational Decoding via Isotropic and Proximal Search (2023)

General-purpose text decoding approaches are usually adopted for dialogue response generation. Although the quality of the generated responses can be improved with dialogue-specific encoding methods, conversational decoding methods are still under-explored. Inspired by SimDRC that a good dialogue feature space should follow the rules of locality and isotropy, we present a fine-grained conversational decoding method, termed isotropic and proximal search (IPS). Our method is designed to generate the semantic-concentrated response, while still maintaining informativeness and discrimination against the context. Experiments show that our approach significantly outperforms existing decoding strategies in the dialogue field across both automatic and human evaluation metrics. More in-depth analyses further confirm the effectiveness of our approach.
Holistic Inter-Annotator Agreement and Corpus Coherence Estimation in a Large-scale Multilingual Annotation Campaign (2023)

In this paper we report on the complexity of persuasion technique annotation in the context of a large multilingual annotation campaign involving 6 languages and approximately 40 annotators. We highlight the techniques that appear to be difficult for humans to annotate and elaborate on our findings on the causes of this phenomenon. We introduce Holistic IAA, a new word embedding-based annotator agreement metric and we report on various experiments using this metric and its correlation with the traditional Inter Annotator Agreement (IAA) metrics. However, given somewhat limited and loose interaction between annotators, i.e., only a few annotators annotate the same document subsets, we try to devise a way to assess the coherence of the entire dataset and strive to find a good proxy for IAA between annotators tasked to annotate different documents and in different languages, for which classical IAA metrics can not be applied.
PHD: Pixel-Based Language Modeling of Historical Documents (2023)

The digitisation of historical documents has provided historians with unprecedented research opportunities. Yet, the conventional approach to analysing historical documents involves converting them from images to text using OCR, a process that overlooks the potential benefits of treating them as images and introduces high levels of noise. To bridge this gap, we take advantage of recent advancements in pixel-based language models trained to reconstruct masked patches of pixels instead of predicting token distributions. Due to the scarcity of real historical scans, we propose a novel method for generating synthetic scans to resemble real historical documents. We then pre-train our model, PHD, on a combination of synthetic scans and real historical newspapers from the 1700-1900 period. Through our experiments, we demonstrate that PHD exhibits high proficiency in reconstructing masked image patches and provide evidence of our model`s noteworthy language understanding capabilities. Notably, we successfully apply our model to a historical QA task, highlighting its usefulness in this domain.
Primacy Effect of ChatGPT (2023)

Instruction-tuned large language models (LLMs), such as ChatGPT, have led to promising zero-shot performance in discriminative natural language understanding (NLU) tasks. This involves querying the LLM using a prompt containing the question, and the candidate labels to choose from. The question-answering capabilities of ChatGPT arise from its pre-training on large amounts of human-written text, as well as its subsequent fine-tuning on human preferences, which motivates us to ask: Does ChatGPT also inherit humans' cognitive biases? In this paper, we study the primacy effect of ChatGPT: the tendency of selecting the labels at earlier positions as the answer. We have two main findings: i) ChatGPT`s decision is sensitive to the order of labels in the prompt; ii) ChatGPT has a clearly higher chance to select the labels at earlier positions as the answer. We hope that our experiments and analyses provide additional insights into building more reliable ChatGPT-based solutions. We release the source code at https://github.com/wangywUST/PrimacyEffectGPT.
Evaluating the Rationale Understanding of Critical Reasoning in Logical Reading Comprehension (2023)

To precisely evaluate a language model`s capability for logical reading comprehension, we present a dataset for testing the understanding of the rationale behind critical reasoning. For questions taken from an existing multiple-choice logical reading comprehension dataset, we crowdsource rationale texts that explain why we should select or eliminate answer options, resulting in 3,003 multiple-choice subquestions that are associated with 943 main questions. Experiments on our dataset show that recent large language models (e.g., InstructGPT) struggle to answer the subquestions even if they are able to answer the main questions correctly. We find that the models perform particularly poorly in answering subquestions written for the incorrect options of the main questions, implying that the models have a limited capability for explaining why incorrect alternatives should be eliminated. These results suggest that our dataset encourages further investigation into the critical reasoning ability of language models while focusing on the elimination process of relevant alternatives.
Evaluating and Modeling Attribution for Cross-Lingual Question Answering (2023)

Trustworthy answer content is abundant in many high-resource languages and is instantly accessible through question answering systems {---} yet this content can be hard to access for those that do not speak these languages. The leap forward in cross-lingual modeling quality offered by generative language models offers much promise, yet their raw generations often fall short in factuality. To improve trustworthiness in these systems, a promising direction is to attribute the answer to a retrieved source, possibly in a content-rich language different from the query. Our work is the first to study attribution for cross-lingual question answering. First, we collect data in 5 languages to assess the attribution level of a state-of-the-art cross-lingual QA system. To our surprise, we find that a substantial portion of the answers is not attributable to any retrieved passages (up to 50{\%} of answers exactly matching a gold reference) despite the system being able to attend directly to the retrieved text. Second, to address this poor attribution level, we experiment with a wide range of attribution detection techniques. We find that Natural Language Inference models and PaLM 2 fine-tuned on a very small amount of attribution data can accurately detect attribution. With these models, we improve the attribution level of a cross-lingual QA system. Overall, we show that current academic generative cross-lingual QA systems have substantial shortcomings in attribution and we build tooling to mitigate these issues.