pwc-archive/papers-with-abstracts huggingface dataset:
Perturbation-Invariant Adversarial Training for Neural Ranking Models: Improving the Effectiveness-Robustness Trade-Off
Authors: Yu-An Liu, Ruqing Zhang, Mingkun Zhang, Wei Chen, Maarten de Rijke, Jiafeng Guo, Xueqi Cheng
Date: 2023-12-16 00:00:00
Tasks: Adversarial Robustness, Information Retrieval

Neural ranking models (NRMs) have shown great success in information retrieval (IR). But their predictions can easily be manipulated using adversarial examples, which are crafted by adding imperceptible perturbations to legitimate documents. This vulnerability raises significant concerns about their reliability and hinders the widespread deployment of NRMs. By incorporating adversarial examples into training data, adversarial training has become the de facto defense approach to adversarial attacks against NRMs. However, this defense mechanism is subject to a trade-off between effectiveness and adversarial robustness. In this study, we establish theoretical guarantees regarding the effectiveness-robustness trade-off in NRMs. We decompose the robust ranking error into two components, i.e., a natural ranking error for effectiveness evaluation and a boundary ranking error for assessing adversarial robustness. Then, we define the perturbation invariance of a ranking model and prove it to be a differentiable upper bound on the boundary ranking error for attainable computation. Informed by our theoretical analysis, we design a novel \emph{perturbation-invariant adversarial training} (PIAT) method for ranking models to achieve a better effectiveness-robustness trade-off. We design a regularized surrogate loss, in which one term encourages the effectiveness to be maximized while the regularization term encourages the output to be smooth, so as to improve adversarial robustness. Experimental results on several ranking models demonstrate the superiority of PITA compared to existing adversarial defenses.
Continuous Prompt Generation from Linear Combination of Discrete Prompt Embeddings
Authors: Pascal Passigan, Kidus Yohannes, Joshua Pereira
Date: 2023-12-16 00:00:00
Tasks: Natural Language Understanding

The wayward quality of continuous prompts stresses the importance of their interpretability as unexpected and unpredictable behaviors appear following training, especially in the context of large language models automating people-sensitive tasks such as resume screening. In this paper we present a novel method of constructing continuous prompts via discrete prompt embeddings and evaluate improvements to continuous prompt interpretability and inference accuracy. For a set of manually designed discrete prompts $\mathcal{D}$, which we tokenize and embed each into tensor form, we train a model to predict the weights such that the linear combinations of those prompts correspond to higher performance on natural language understanding tasks.
LLM-SQL-Solver: Can LLMs Determine SQL Equivalence?
Authors: Fuheng Zhao, Jiayue Chen, Lawrence Lim, Ishtiyaque Ahmad, Divyakant Agrawal, Amr El Abbadi
Date: 2023-12-16 00:00:00
Tasks: Question Answering, Text to SQL, Text-To-SQL

Judging the equivalence between two SQL queries is a fundamental problem with many practical applications in data management and SQL generation (i.e., evaluating the quality of generated SQL queries in text-to-SQL task). While the research community has reasoned about SQL equivalence for decades, it poses considerable difficulties and no complete solutions exist. Recently, Large Language Models (LLMs) have shown strong reasoning capability in conversation, question answering and solving mathematics challenges. In this paper, we study if LLMs can be used to determine the equivalence between SQL queries under two notions of SQL equivalence (semantic equivalence and relaxed equivalence). To assist LLMs in generating high quality responses, we present two prompting techniques: Miniature & Mull and Explain & Compare. The former technique is used to evaluate the semantic equivalence in which it asks LLMs to execute a query on a simple database instance and then explore if a counterexample exists by modifying the database. The latter technique is used to evaluate the relaxed equivalence in which it asks LLMs to explain the queries and then compare if they contain significant logical differences. Our experiments demonstrate using our techniques, LLMs is a promising tool to help data engineers in writing semantically equivalent SQL queries, however challenges still persist, and is a better metric for evaluating SQL generation than the popular execution accuracy.
Catwalk: A Unified Language Model Evaluation Framework for Many Datasets
Authors: Dirk Groeneveld, Anas Awadalla, Iz Beltagy, Akshita Bhagia, Ian Magnusson, Hao Peng, Oyvind Tafjord, Pete Walsh, Kyle Richardson, Jesse Dodge
Date: 2023-12-15 00:00:00
Tasks: In-Context Learning, Language Model Evaluation, Language Modeling, Language Modelling

The success of large language models has shifted the evaluation paradigms in natural language processing (NLP). The community's interest has drifted towards comparing NLP models across many tasks, domains, and datasets, often at an extreme scale. This imposes new engineering challenges: efforts in constructing datasets and models have been fragmented, and their formats and interfaces are incompatible. As a result, it often takes extensive (re)implementation efforts to make fair and controlled comparisons at scale. Catwalk aims to address these issues. Catwalk provides a unified interface to a broad range of existing NLP datasets and models, ranging from both canonical supervised training and fine-tuning, to more modern paradigms like in-context learning. Its carefully-designed abstractions allow for easy extensions to many others. Catwalk substantially lowers the barriers to conducting controlled experiments at scale. For example, we finetuned and evaluated over 64 models on over 86 datasets with a single command, without writing any code. Maintained by the AllenNLP team at the Allen Institute for Artificial Intelligence (AI2), Catwalk is an ongoing open-source effort: https://github.com/allenai/catwalk.
VK-G2T: Vision and Context Knowledge enhanced Gloss2Text
Authors: Liqiang Jing, Xuemeng Song, Xinxing Zu, Na Zheng, Zhongzhou Zhao, Liqiang Nie
Date: 2023-12-15 00:00:00
Tasks: Sentence, Sign Language Translation, Translation

Existing sign language translation methods follow a two-stage pipeline: first converting the sign language video to a gloss sequence (i.e. Sign2Gloss) and then translating the generated gloss sequence into a spoken language sentence (i.e. Gloss2Text). While previous studies have focused on boosting the performance of the Sign2Gloss stage, we emphasize the optimization of the Gloss2Text stage. However, this task is non-trivial due to two distinct features of Gloss2Text: (1) isolated gloss input and (2) low-capacity gloss vocabulary. To address these issues, we propose a vision and context knowledge enhanced Gloss2Text model, named VK-G2T, which leverages the visual content of the sign language video to learn the properties of the target sentence and exploit the context knowledge to facilitate the adaptive translation of gloss words. Extensive experiments conducted on a Chinese benchmark validate the superiority of our model.
Low-resource classification of mobility functioning information in clinical sentences using large language models
Authors: Tuan Dung Le, Thanh Duong, Thanh Thieu
Date: 2023-12-15 00:00:00
Tasks: Binary Classification, In-Context Learning, NER, Sentence

Objective: Function is increasingly recognized as an important indicator of whole-person health. This study evaluates the ability of publicly available large language models (LLMs) to accurately identify the presence of functioning information from clinical notes. We explore various strategies to improve the performance on this task. Materials and Methods: We collect a balanced binary classification dataset of 1000 sentences from the Mobility NER dataset, which was curated from n2c2 clinical notes. For evaluation, we construct zero-shot and few-shot prompts to query the LLMs whether a given sentence contains mobility functioning information. Two sampling techniques, random sampling and k-nearest neighbor (kNN)-based sampling, are used to select the few-shot examples. Furthermore, we apply a parameter-efficient prompt-based fine-tuning method to the LLMs and evaluate their performance under various training settings. Results: Flan-T5-xxl outperforms all other models in both zero-shot and few-shot settings, achieving a F1 score of 0.865 with a single demonstrative example selected by kNN sampling. In prompt-based fine-tuning experiments, this foundation model also demonstrates superior performance across all low-resource settings, particularly achieving an impressive F1 score of 0.922 using the full training dataset. The smaller model, Flan-T5-xl, requires fine-tuning with only 2.3M additional parameters to achieve comparable performance to the fully fine-tuned Gatortron-base model, both surpassing 0.9 F1 score. Conclusion: Open-source instruction-tuned LLMs demonstrate impressive in-context learning capability in the mobility functioning classification task. The performance of these models can be further improved by continuing fine-tuning on a task-specific dataset.
Pipeline and Dataset Generation for Automated Fact-checking in Almost Any Language
Authors: Jan Drchal, Herbert Ullrich, Tomáš Mlynář, Václav Moravec
Date: 2023-12-15 00:00:00
Tasks: Dataset Generation, Fact Checking, Machine Translation, Question Answering

This article presents a pipeline for automated fact-checking leveraging publicly available Language Models and data. The objective is to assess the accuracy of textual claims using evidence from a ground-truth evidence corpus. The pipeline consists of two main modules -- the evidence retrieval and the claim veracity evaluation. Our primary focus is on the ease of deployment in various languages that remain unexplored in the field of automated fact-checking. Unlike most similar pipelines, which work with evidence sentences, our pipeline processes data on a paragraph level, simplifying the overall architecture and data requirements. Given the high cost of annotating language-specific fact-checking training data, our solution builds on the Question Answering for Claim Generation (QACG) method, which we adapt and use to generate the data for all models of the pipeline. Our strategy enables the introduction of new languages through machine translation of only two fixed datasets of moderate size. Subsequently, any number of training samples can be generated based on an evidence corpus in the target language. We provide open access to all data and fine-tuned models for Czech, English, Polish, and Slovak pipelines, as well as to our codebase that may be used to reproduce the results.We comprehensively evaluate the pipelines for all four languages, including human annotations and per-sample difficulty assessment using Pointwise V-information. The presented experiments are based on full Wikipedia snapshots to promote reproducibility. To facilitate implementation and user interaction, we develop the FactSearch application featuring the proposed pipeline and the preliminary feedback on its performance.
Review of Unsupervised POS Tagging and Its Implications on Language Acquisition
Authors: Niels Dickson
Date: 2023-12-15 00:00:00
Tasks: Language Acquisition, POS, POS Tagging

An ability that underlies human syntactic knowledge is determining which words can appear in the similar structures (i.e. grouping words by their syntactic categories). These groupings enable humans to combine structures in order to communicate complex meanings. A foundational question is how do children acquire this ability underlying syntactic knowledge. In exploring this process, we will review various engineering approaches whose goal is similar to that of a child's -- without prior syntactic knowledge, correctly identify the parts of speech (POS) of the words in a sample of text. In reviewing these unsupervised tagging efforts, we will discuss common themes that support the advances in the models and their relevance for language acquisition. For example, we discuss how each model judges success (evaluation metrics), the "additional information" that constrains the POS learning (such as orthographic information), and the context used to determine POS (only previous word, words before and after the target, etc). The identified themes pave the way for future investigations into the cognitive processes that underpin the acquisition of syntactic categories and provide a useful layout of current state of the art unsupervised POS tagging models.
Do LVLMs Understand Charts? Analyzing and Correcting Factual Errors in Chart Captioning
Authors: Kung-Hsiang Huang, Mingyang Zhou, Hou Pong Chan, Yi R. Fung, Zhenhailong Wang, Lingyu Zhang, Shih-Fu Chang, Heng Ji
Date: 2023-12-15 00:00:00
Tasks: Factual Inconsistency Detection in Chart Captioning, Image Captioning, Visual Entailment

Recent advancements in large vision-language models (LVLMs) have led to significant progress in generating natural language descriptions for visual content and thus enhancing various applications. One issue with these powerful models is that they sometimes produce texts that are factually inconsistent with the visual input. While there has been some effort to mitigate such inconsistencies in natural image captioning, the factuality of generated captions for structured document images, such as charts, has not received as much scrutiny, posing a potential threat to information reliability in critical applications. This work delves into the factuality aspect by introducing a comprehensive typology of factual errors in generated chart captions. A large-scale human annotation effort provides insight into the error patterns and frequencies in captions crafted by various chart captioning models, ultimately forming the foundation of a novel dataset, CHOCOLATE. Our analysis reveals that even state-of-the-art models, including GPT-4V, frequently produce captions laced with factual inaccuracies. In response to this challenge, we establish the new task of Chart Caption Factual Error Correction and introduce CHARTVE, a model for visual entailment that outperforms proprietary and open-source LVLMs in evaluating factual consistency. Furthermore, we propose C2TFEC, an interpretable two-stage framework that excels at correcting factual errors. This work inaugurates a new domain in factual error correction for chart captions, presenting a novel evaluation mechanism, and demonstrating an effective approach to ensuring the factuality of generated chart captions. The code and data as well as the continuously updated benchmark can be found at: https://khuangaf.github.io/CHOCOLATE/.
Do Text Simplification Systems Preserve Meaning? A Human Evaluation via Reading Comprehension
Authors: Sweta Agrawal, Marine Carpuat
Date: 2023-12-15 00:00:00
Tasks: Question Answering, Reading Comprehension, Text Simplification

Automatic text simplification (TS) aims to automate the process of rewriting text to make it easier for people to read. A pre-requisite for TS to be useful is that it should convey information that is consistent with the meaning of the original text. However, current TS evaluation protocols assess system outputs for simplicity and meaning preservation without regard for the document context in which output sentences occur and for how people understand them. In this work, we introduce a human evaluation framework to assess whether simplified texts preserve meaning using reading comprehension questions. With this framework, we conduct a thorough human evaluation of texts by humans and by nine automatic systems. Supervised systems that leverage pre-training knowledge achieve the highest scores on the reading comprehension (RC) tasks amongst the automatic controllable TS systems. However, even the best-performing supervised system struggles with at least 14% of the questions, marking them as "unanswerable'' based on simplified content. We further investigate how existing TS evaluation metrics and automatic question-answering systems approximate the human judgments we obtained.
A Review of Repository Level Prompting for LLMs
Authors: Douglas Schonholtz
Date: 2023-12-15 00:00:00
Tasks: Code Completion, Code Generation, HumanEval, Retrieval

As coding challenges become more complex, recent advancements in Large Language Models (LLMs) have led to notable successes, such as achieving a 94.6\% solve rate on the HumanEval benchmark. Concurrently, there is an increasing commercial push for repository-level inline code completion tools, such as GitHub Copilot and Tab Nine, aimed at enhancing developer productivity. This paper delves into the transition from individual coding problems to repository-scale solutions, presenting a thorough review of the current literature on effective LLM prompting for code generation at the repository level. We examine approaches that will work with black-box LLMs such that they will be useful and applicable to commercial use cases, and their applicability in interpreting code at a repository scale. We juxtapose the Repository-Level Prompt Generation technique with RepoCoder, an iterative retrieval and generation method, to highlight the trade-offs inherent in each approach and to establish best practices for their application in cutting-edge coding benchmarks. The interplay between iterative refinement of prompts and the development of advanced retrieval systems forms the core of our discussion, offering a pathway to significantly improve LLM performance in code generation tasks. Insights from this study not only guide the application of these methods but also chart a course for future research to integrate such techniques into broader software engineering contexts.
Arithmetics-Based Decomposition of Numeral Words -- Arithmetic Conditions give the Unpacking Strategy
Authors: Isidor Konrad Maier, Matthias Wolff
Date: 2023-12-14 00:00:00

This paper presents a novel numeral decomposer based on arithmetic criteria. The criteria are not dependent on a base-10 assumption but only on Hurford's Packing Strategy. Hurford's Packing Strategy constitutes numerals by packing factors and summands to multiplicators. We found out that a numeral of value n has a multiplicator larger than sqrt(n), a summand smaller than n/2 and a factor smaller than sqrt(n). Using these findings, the numeral decomposer attempts to detect and unpack factors and summand in order to reverse Hurford's Packing strategy. We tested its applicability for incremental unsupervised grammar induction in 273 languages. This way, grammars were obtained with sensible mathematical attributes that explain the structure of produced numerals. The numeral-decomposer-induced grammars are often close to expert-made and more compact than numeral grammars induced by a modern state-of-the-art grammar induction tool. Furthermore, this paper contains a report about the few cases of incorrect induced mathematical attributes, which are often linked to linguistic peculiarities like context sensitivity.
Look Before You Leap: A Universal Emergent Decomposition of Retrieval Tasks in Language Models
Authors: Alexandre Variengien, Eric Winsor
Date: 2023-12-13 00:00:00
Tasks: Attribute, Question Answering, Retrieval

When solving challenging problems, language models (LMs) are able to identify relevant information from long and complicated contexts. To study how LMs solve retrieval tasks in diverse situations, we introduce ORION, a collection of structured retrieval tasks spanning six domains, from text understanding to coding. Each task in ORION can be represented abstractly by a request (e.g. a question) that retrieves an attribute (e.g. the character name) from a context (e.g. a story). We apply causal analysis on 18 open-source language models with sizes ranging from 125 million to 70 billion parameters. We find that LMs internally decompose retrieval tasks in a modular way: middle layers at the last token position process the request, while late layers retrieve the correct entity from the context. After causally enforcing this decomposition, models are still able to solve the original task, preserving 70% of the original correct token probability in 98 of the 106 studied model-task pairs. We connect our macroscopic decomposition with a microscopic description by performing a fine-grained case study of a question-answering task on Pythia-2.8b. Building on our high-level understanding, we demonstrate a proof of concept application for scalable internal oversight of LMs to mitigate prompt-injection while requiring human supervision on only a single input. Our solution improves accuracy drastically (from 15.5% to 97.5% on Pythia-12b). This work presents evidence of a universal emergent modular processing of tasks across varied domains and models and is a pioneering effort in applying interpretability for scalable internal oversight of LMs.
Runtime Analysis of the SMS-EMOA for Many-Objective Optimization
Authors: Weijie Zheng, Benjamin Doerr
Date: 2023-12-16 00:00:00
Tasks: 2k

The classic NSGA-II was recently proven to have considerable difficulties in many-objective optimization. This paper conducts the first rigorous runtime analysis in many objectives for the SMS-EMOA, a steady-state NSGA-II that uses the hypervolume contribution instead of the crowding distance as the second selection criterion. To this aim, we first propose a many-objective counterpart, the m-objective mOJZJ, of the bi-objective OJZJ, which is the first many-objective multimodal benchmark for runtime analysis. We prove that SMS-EMOA computes the full Pareto front of this benchmark in an expected number of $O(\mu M n^k)$ iterations, where $n$ denotes the problem size (length of the bit-string representation), $k$ the gap size (a difficulty parameter of the problem), $M=(2n/m-2k+3)^{m/2}$ the size of the Pareto front, and $\mu$ the population size (at least the same size as the largest incomparable set). This result together with the existing negative result for the original NSGA-II shows that, in principle, the general approach of the NSGA-II is suitable for many-objective optimization, but the crowding distance as tie-breaker has deficiencies. We obtain three additional insights on the SMS-EMOA. Different from a recent result for the bi-objective OJZJ benchmark, a recently proposed stochastic population update often does not help for mOJZJ. It at most results in a speed-up by a factor of order $2^{k} / \mu$, which is $\Theta(1)$ for large $m$, such as $m>k$. On the positive side, we prove that heavy-tailed mutation irrespective of the number $m$ of objectives results in a speed-up of order $k^{0.5+k-\beta}/e^k$, the same advantage as previously shown for the bi-objective case. Finally, we conduct the first runtime analyses of the SMS-EMOA on the classic bi-objective OneMinMax and LOTZ benchmarks and show that the SMS-EMOA has a performance comparable to the GSEMO and the NSGA-II.