ymoslem/acl-abstracts huggingface dataset:
Comparing Kaldi-Based Pipeline Elpis and Whisper for \vCakavian Transcription (2024)

Automatic speech recognition (ASR) has the potential to accelerate the documentation of endangered languages, but the dearth of resources poses a major obstacle. {\v{C}}akavian, an endangered variety spoken primarily in Croatia, is a case in point, lacking transcription tools that could aid documentation efforts. We compare training a new ASR model on a limited dataset using the Kaldi-based ASR pipeline Elpis to using the same dataset to adapt the transformer-based pretrained multilingual model Whisper, to determine which is more practical in the documentation context. Results show that Whisper outperformed Elpis, achieving the lowest average Word Error Rate (WER) of 57.3{\%} and median WER of 35.48{\%}. While Elpis offers a less computationally expensive model and friendlier user experience, Whisper appears better at adapting to our collected {\v{C}}akavian data.
Zero-shot Cross-lingual POS Tagging for Filipino (2024)

Supervised learning approaches in NLP, exemplified by POS tagging, rely heavily on the presence of large amounts of annotated data. However, acquiring such data often requires significant amount of resources and incurs high costs. In this work, we explore zero-shot cross-lingual transfer learning to address data scarcity issues in Filipino POS tagging, particularly focusing on optimizing source language selection. Our zero-shot approach demonstrates superior performance compared to previous studies, with top-performing fine-tuned PLMs achieving F1 scores as high as 79.10{\%}. The analysis reveals moderate correlations between cross-lingual transfer performance and specific linguistic distances{--}featural, inventory, and syntactic{--}suggesting that source languages with these features closer to Filipino provide better results. We identify tokenizer optimization as a key challenge, as PLM tokenization sometimes fails to align with meaningful representations, thus hindering POS tagging performance.
The Automated Verification of Textual Claims (AVeriTeC) Shared Task (2024)

The Automated Verification of Textual Claims (AVeriTeC) shared task asks participants to retrieve evidence and predict veracity for real-world claims checked by fact-checkers. Evidence can be found either via a search engine, or via a knowledge store provided by the organisers. Submissions are evaluated using the AVeriTeC score, which considers a claim to be accurately verified if and only if both the verdict is correct and retrieved evidence is considered to meet a certain quality threshold. The shared task received 21 submissions, 18 of which surpassed our baseline. The winning team was TUDA{\_}MAI with an AVeriTeC score of 63{\%}. In this paper we describe the shared task, present the full results, and highlight key takeaways from the shared task.
Multi-hop Evidence Pursuit Meets the Web: Team Papelo at FEVER 2024 (2024)

Separating disinformation from fact on the web has long challenged both the search and the reasoning powers of humans. We show that the reasoning power of large language models (LLMs) and the retrieval power of modern search engines can be combined to automate this process and explainably verify claims. We integrate LLMs and search under a multi-hop evidence pursuit strategy. This strategy generates an initial question based on an input claim using a sequence to sequence model, searches and formulates an answer to the question, and iteratively generates follow-up questions to pursue the evidence that is missing using an LLM. We demonstrate our system on the FEVER 2024 (AVeriTeC) shared task. Compared to a strategy of generating all the questions at once, our method obtains .045 higher label accuracy and .155 higher AVeriTeC score (evaluating the adequacy of the evidence). Through ablations, we show the importance of various design choices, such as the question generation method, medium-sized context, reasoning with one document at a time, adding metadata, paraphrasing, reducing the problem to two classes, and reconsidering the final verdict. Our submitted system achieves .510 AVeriTeC score on the dev set and .477 AVeriTec score on the test set.
Retrieving Semantics for Fact-Checking: A Comparative Approach using CQ (Claim to Question) \& AQ (Answer to Question) (2024)

Fact-checking using evidences is the preferred way to tackle the issue of misinformation in the society. The democratization of information through social media has accelerated the spread of information, allowing misinformation to reach and influence a vast audience. The significant impact of these falsehoods on society and public opinion underscores the need for automated approaches to identify and combat this phenomenon.This paper is describes the participation of team IKR3-UNIMIB in AVeriTeC (Automated Verification of Textual Claims) 2024 shared task. We proposed a methods to retrieve evidence in the question and answer format and predict the veracity of a claim. As part of the AVeriTeC shared task, our method combines similarity-based ColBERT re-ranker with traditional keyword search using BM25. Additionally, a recent promising approach, Chain of RAG (CoRAG) is introduced to generate question and answer pairs (QAs) to evaluate performance on this specific dataset. We explore whether generating questions from claims or answers produces more effective QA pairs for veracity prediction. Additionally, we try to generate questions from the claim rather than from evidence (opposite the AVeriTeC dataset paper) to generate effective QA pairs for veracity prediction. Our method achieved an AVeriTeC Score of 0.18 (more than baseline) on the test dataset, demonstrating its potential in automated fact-checking.
RAG-Fusion Based Information Retrieval for Fact-Checking (2024)

Fact-checking involves searching for relevant evidence and determining whether the given claim contains any misinformation. In this paper, we propose a fact verification system based on RAG-Fusion. We use GPT-4o to generate questions from the claim, which helps improve the accuracy of evidence retrieval.Additionally, we adopt GPT-4o for the final judgment module and refine the prompts to enhance the detection accuracy, particularly when the claim contains misinformation. Experiment showed that our system achieved an AVeriTeC Score of 0.3865 on the AVeriTeC test data, significantly surpassing the baseline score of 0.11.
UHH at AVeriTeC: RAG for Fact-Checking with Real-World Claims (2024)

This paper presents UHH`s approach developed for the AVeriTeC shared task. The goal of the challenge is to verify given real-world claims with evidences from the Web. In this shared task, we investigate a Retrieval-Augmented Generation (RAG) model, which mainly contains retrieval, generation, and augmentation components. We start with the selection of the top 10k evidences via BM25 scores, and continue with two approaches to retrieve the most similar evidences: (1) to retrieve top 10 evidences through vector similarity, generate questions for them, and rerank them or (2) to generate questions for the claim and retrieve the most similar evidence, again, through vector similarity. After retrieving the top evidences, a Large Language Model (LLM) is prompted using the claim along with either all evidences or individual evidence to predict the label. Our system submission, $\textbf{UHH}$, using the first approach and individual evidence prompts, ranks 6th out of 23 systems.
Improving Evidence Retrieval on Claim Verification Pipeline through Question Enrichment (2024)

The AVeriTeC shared task introduces a new real-word claim verification dataset, where a system is tasked to verify a real-world claim based on the evidence found in the internet.In this paper, we proposed a claim verification pipeline called QueenVer which consists of 2 modules, Evidence Retrieval and Claim Verification.Our pipeline collects pairs of {\ensuremath{<}}Question, Answer{\ensuremath{>}} as the evidence. Recognizing the pivotal role of question quality in the evidence efficacy, we proposed question enrichment to enhance the retrieved evidence. Specifically, we adopt three different Question Generation (QG) technique, muti-hop, single-hop, and Fact-checker style. For the claim verification module, we integrate an ensemble of multiple state-of-the-art LLM to enhance its robustness.Experiments show that QueenVC achieves 0.41, 0.29, and 0.42 on Q, Q+A, and AVeriTeC scores.
Dunamu-ml`s Submissions on AVERITEC Shared Task (2024)

This paper presents the Dunamu-ml`s submission to the AVERITEC shared task of the 7th the Fact Extraction and VERification (FEVER) workshop. The task focused on discriminating whether each claim is a fact or not. Our method is powered by the combination of an LLM and a non-parametric lexicon-based method (i.e. BM25). Essentially, we augmented the list of evidences containing the query and the corresponding answers using an powerful LLM, then, retrieved the relative documents using the generated evidences. As such, our method made a great improvement over the baseline results, achieving 0.33 performance gain over the baseline in AveriTec score.
FZI-WIM at AVeriTeC Shared Task: Real-World Fact-Checking with Question Answering (2024)

This paper describes the FZI-WIM system at the AVeriTeC shared Task, which aims to assess evidence-based automated fact-checking systems for real-world claims with evidence retrieved from the web. The FZI-WIM system utilizes open-source models to build a reliable fact-checking pipeline via question-answering. With different experimental setups, we show that more questions lead to higher scores in the shared task. Both in question generation and question-answering stages, sampling can be a way to improve the performance of our system. We further analyze the limitations of current open-source models for real-world claim verification. Our code is publicly available https://github.com/jens5588/FZI-WIM-AVERITEC.
Zero-Shot Learning and Key Points Are All You Need for Automated Fact-Checking (2024)

Automated fact-checking is an important task because determining the accurate status of a proposed claim within the vast amount of information available online is a critical challenge. This challenge requires robust evaluation to prevent the spread of false information. Modern large language models (LLMs) have demonstrated high capability in performing a diverse range of Natural Language Processing (NLP) tasks. By utilizing proper prompting strategies, their versatility{---}due to their understanding of large context sizes and zero-shot learning ability{---}enables them to simulate human problem-solving intuition and move towards being an alternative to humans for solving problems. In this work, we introduce a straightforward framework based on {\_}**Z**ero-**S**hot **L**earning{\_} and {\_}**Ke**y **P**oints{\_} (ZSL-KeP) for automated fact-checking, which despite its simplicity, performed well on the AVeriTeC shared task dataset by robustly improving the baseline and achieving 10th place.
Evidence-backed Fact Checking using RAG and Few-Shot In-Context Learning with LLMs (2024)

Given the widespread dissemination of misinformation on social media, implementing fact-checking mechanisms for online claims is essential. Manually verifying every claim is very challenging, underscoring the need for an automated fact-checking system. This paper presents our system designed to address this issue. We utilize the Averitec dataset (Schlichtkrull et al., 2023) to assess the performance of our fact-checking system. In addition to veracity prediction, our system provides supporting evidence, which is extracted from the dataset. We develop a Retrieve and Generate (RAG) pipeline to extract relevant evidence sentences from a knowledge base, which are then inputted along with the claim into a large language model (LLM) for classification. We also evaluate the few-shot In-Context Learning (ICL) capabilities of multiple LLMs. Our system achieves an {\textquoteleft}Averitec' score of 0.33, which is a 22{\%} absolute improvement over the baseline. Our Code is publicly available on https://github.com/ronit-singhal/evidence-backed-fact-checking-using-rag-and-few-shot-in-context-learning-with-llms.
SK\_DU Team: Cross-Encoder based Evidence Retrieval and Question Generation with Improved Prompt for the AVeriTeC Shared Task (2024)

As part of the AVeriTeC shared task, we developed a pipelined system comprising robust and finely tuned models. Our system integrates advanced techniques for evidence retrieval and question generation, leveraging cross-encoders and large language models (LLMs) for optimal performance. With multi-stage processing, the pipeline demonstrates improvements over baseline models, particularly in handling complex claims that require nuanced reasoning by improved evidence extraction, question generation and veracity prediction. Through detailed experiments and ablation studies, we provide insights into the strengths and weaknesses of our approach, highlighting the critical role of evidence sufficiency and context dependency in automated fact-checking systems. Our system secured a competitive rank, 7th on the development and 12th on the test data, in the shared task, underscoring the effectiveness of our methods in addressing the challenges of real-world claim verification.
InFact: A Strong Baseline for Automated Fact-Checking (2024)

The spread of disinformation poses a global threat to democratic societies, necessitating robust and scalable Automated Fact-Checking (AFC) systems. The AVeriTeC Shared Task Challenge 2024 offers a realistic benchmark for text-based fact-checking methods. This paper presents Information-Retrieving Fact-Checker (InFact), an LLM-based approach that breaks down the task of claim verification into a 6-stage process, including evidence retrieval. When using GPT-4o as the backbone, InFact achieves an AVeriTeC score of 63{\%} on the test set, outperforming all other 20 teams competing in the challenge, and establishing a new strong baseline for future text-only AFC systems. Qualitative analysis of mislabeled instances reveals that InFact often yields a more accurate conclusion than AVeriTeC`s human-annotated ground truth.
Exploring Retrieval Augmented Generation For Real-world Claim Verification (2024)

Automated Fact-Checking (AFC) has recently gained considerable attention to address the increasing misinformation spreading in the web and social media. The recently introduced AVeriTeC dataset alleviates some limitations of existing AFC benchmarks. In this paper, we propose to explore Retrieval Augmented Generation (RAG) and describe the system (UPS participant) we implemented to solve the AVeriTeC shared task.Our end-to-end system integrates retrieval and generation in a joint training setup to enhance evidence retrieval and question generation. Our system operates as follows: First, we conduct dense retrieval of evidence by encoding candidate evidence sentences from the provided knowledge store documents. Next, we perform a secondary retrieval of question-answer pairs from the training set, encoding these into dense vectors to support question generation with relevant in-context examples. During training, the question generator is optimized to generate questions based on retrieved or gold evidence. In preliminary automatic evaluation, our system achieved respectively 0.198 and 0.210 AVeriTeC scores on the dev and test sets.
GProofT: A Multi-dimension Multi-round Fact Checking Framework Based on Claim Fact Extraction (2024)

In the information era, the vast proliferation of online content poses significant challenges, particularly concerning the trustworthiness of these digital statements, which can have profound societal implications. Although it is possible to manually annotate and verify the authenticity of such content, the sheer volume and rapid pace of information generation render this approach impractical, both in terms of time and cost. Therefore, it is imperative to develop automated systems capable of validating online claims, ensuring that users can use the wealth of information available on the Internet effectively and reliably. Using primarily ChatGPT and the Google search API, GProofT fact checking framework generates question-answer pairs to systematically extract and verify the facts within claims. Based on the outcomes of these QA pairs, claims are subsequently labeled as Supported, Conflicted Evidence/Cherry-Picking, or Refuted. Shown by extensive experiments, GProofT Retrieval generally performs effectively in fact-checking and makes a substantial contribution to the task. Our code is released on https://github.com/HKUST-KnowComp/GProofT.
HerO at AVeriTeC: The Herd of Open Large Language Models for Verifying Real-World Claims (2024)

To tackle the AVeriTeC shared task hosted by the FEVER-24, we introduce a system that only employs publicly available large language models (LLMs) for each step of automated fact-checking, dubbed the \textbf{Her}d of \textbf{O}pen LLMs for verifying real-world claims (\textbf{HerO}). HerO employs multiple LLMs for each step of automated fact-checking. For evidence retrieval, a language model is used to enhance a query by generating hypothetical documents that check the veracity of a claim. We fine-tune LLMs for question generation and veracity prediction by crafting prompts with retrieved in-context samples. HerO achieved 2nd place on the leaderboard with the AVeriTeC score of 0.57, suggesting the potential of open LLMs for verifying real-world claims. For future research, we make our code publicly available at \url{https://github.com/ssu-humane/HerO}.
AIC CTU system at AVeriTeC: Re-framing automated fact-checking as a simple RAG task (2024)

This paper describes our $3^{rd}$ place submission in the AVeriTeC shared task in which we attempted to address the challenge of fact-checking with evidence retrieved in the wild using a simple scheme of Retrieval-Augmented Generation (RAG) designed for the task, leveraging the predictive power of Large Language Models.We release our codebase and explain its two modules - the Retriever and the Evidence {\&} Label generator - in detail, justifying their features such as MMR-reranking and Likert-scale confidence estimation.We evaluate our solution on AVeriTeC dev and test set and interpret the results, picking the GPT-4o as the most appropriate model for our pipeline at the time of our publication, with Llama 3.1 70B being a promising open-source alternative.We perform an empirical error analysis to see that faults in our predictions often coincide with noise in the data or ambiguous fact-checks, provoking further research and data augmentation.
Enhancing Fact Verification with Causal Knowledge Graphs and Transformer-Based Retrieval for Deductive Reasoning (2024)

The ability to extract and verify factual information from free-form text is critical in an era where vast amounts of unstructured data are available, yet unreliable sources abound. This paper focuses on enhancing causal deductive reasoning, a key component of factual verification, through the lens of accident investigation, where determining the probable causes of events is paramount. Deductive reasoning refers to the task of drawing conclusions based on a premise. While some deductive reasoning benchmarks exist, none focus on causal deductive reasoning and are from real-world applications. Recently, large language models (LLMs) used with prompt engineering techniques like retrieval-augmented generation (RAG) have demonstrated remarkable performance across various natural language processing benchmarks. However, adapting these techniques to handle scenarios with no knowledge bases and to different data structures, such as graphs, remains an ongoing challenge. In our study, we introduce a novel framework leveraging LLMs' decent ability to detect and infer causal relations to construct a causal Knowledge Graph (KG) which represents knowledge that the LLM recognizes. Additionally, we propose a RoBERTa-based Transformer Graph Neural Network (RoTG) specifically designed to select relevant nodes within this KG. Integrating RoTG-retrieved causal chains into prompts effectively enhances LLM performance, demonstrating usefulness of our approach in advancing LLMs' causal deductive reasoning capabilities.
Numerical Claim Detection in Finance: A New Financial Dataset, Weak-Supervision Model, and Market Analysis (2024)

In this paper, we investigate the influence of claims in analyst reports and earnings calls on financial market returns, considering them as significant quarterly events for publicly traded companies. To facilitate a comprehensive analysis, we construct a new financial dataset for the claim detection task in the financial domain. We benchmark various language models on this dataset and propose a novel weak-supervision model that incorporates the knowledge of subject matter experts (SMEs) in the aggregation function, outperforming existing approaches. We also demonstrate the practical utility of our proposed model by constructing a novel measure of *optimism*. Here, we observe the dependence of earnings surprise and return on our optimism measure. Our dataset, models, and code are publicly (under CC BY 4.0 license) available on GitHub.
Streamlining Conformal Information Retrieval via Score Refinement (2024)

Information retrieval (IR) methods, like retrieval augmented generation, are fundamental to modern applications but often lack statistical guarantees. Conformal prediction addresses this by retrieving sets guaranteed to include relevant information, yet existing approaches produce large-sized sets, incurring high computational costs and slow response times. In this work, we introduce a score refinement method that applies a simple monotone transformation to retrieval scores, leading to significantly smaller conformal sets while maintaining their statistical guarantees. Experiments on various BEIR benchmarks validate the effectiveness of our approach in producing compact sets containing relevant information.
Improving Explainable Fact-Checking via Sentence-Level Factual Reasoning (2024)

Most existing fact-checking systems are unable to explain their decisions by providing relevant rationales (justifications) for their predictions. It highlights a lack of transparency that poses significant risks, such as the prevalence of unexpected biases, which may increase political polarization due to limitations in impartiality. To address this critical gap, we introduce SEntence-Level FActual Reasoning (SELFAR), aimed at improving explainable fact-checking. SELFAR relies on fact extraction and verification by predicting the news source reliability and factuality (veracity) of news articles or claims at the sentence level, generating post-hoc explanations using SHAP/LIME and zero-shot prompts. Our experiments show that unreliable news stories predominantly consist of subjective statements, in contrast to reliable ones. Consequently, predicting unreliable news articles at the sentence level by analyzing impartiality and subjectivity is a promising approach for fact extraction and improving explainable fact-checking. Furthermore, LIME outperforms SHAP in explaining predictions on reliability. Additionally, while zero-shot prompts provide highly readable explanations and achieve an accuracy of 0.71 in predicting factuality, their tendency to hallucinate remains a challenge. Lastly, this paper also presents the first study on explainable fact-checking in the Portuguese language.