Grounded claim factuality checking is important for large language model (LLM) applications such as retrieval-augmented generation, as it helps users assess the correctness of generated outputs.Existing metrics using entailment classifiers require dataset-specific threshold tuning, while LLM-based approaches often use direct prompting, which underutilises the reasoning capabilities of LLMs.We address this by formulating grounded claim factuality checking as a true/false reading comprehension task and prompting LLMs with explicit test-taking strategies for efficient reasoning.Our method reduces token usage by over 80% compared to unguided open-ended reasoning, and achieves competitive performance to more expensive alternatives across two factuality benchmarks, setting a new state of the art on one.To further reduce inference cost, we train small language models (SLMs) to replace LLMs in the checking pipeline.Using supervised fine-tuning (SFT) and a self-revision mechanism, the SLMs learn to improve their factuality judgements.Experimental results show that the resulting SLMs perform on par with strong baselines, combining low inference costs with generating supporting rationales to improve interpretability. 1

Large instruction-following models are powerful but costly to deploy, particularly in finance, where labelled data are limited by confidentiality and expert annotation cost. We present an efficient framework for financial sentiment analysis through distillation with synthetic data, transferring knowledge from a large instruction-tuned teacher to compact student models. The framework is designed for low-resource conditions, where a small set of real examples are collected and labelled by hand. The framework then clusters the examples and uses the clusters to select seeds for generating synthetic examples via structured few-shot prompting. Experiments show that clustering-based seed selection yields more representative synthetic data than random sampling, enabling compact models to achieve strong performance with minimal supervision. Notably, on a more complex and noisy text domain, the compact model trained on the complete synthetic-seed corpus even outperforms the teacher model, while remaining competitive on formal text. The framework provides a practical route toward resource-efficient domain adaptation in financial NLP with minimal human labelling effort.

This paper introduces a novel pipeline for summarising timelines of events reported by multiple news sources. Transformer-based models for abstractive summarisation generate coherent and concise summaries of long documents but can fail to outperform established extractive methods on specialised tasks such as timeline summarisation (TLS). While extractive summaries are more faithful to their sources, they may be less readable and contain redundant or unnecessary information. This paper proposes a preference-based reinforcement learning (PBRL) method for adapting pretrained abstractive summarisers to TLS, which can overcome the drawbacks of extractive timeline summaries. We define a compound reward function that learns from keywords of interest and pairwise preference labels, which we use to fine-tune a pretrained abstractive summariser via offline reinforcement learning. We carry out both automated and human evaluation on three datasets, finding that our method outperforms a comparable extractive TLS method on two of the three benchmark datasets, and participants prefer our method’s summaries to those of both the extractive TLS method and the pretrained abstractive model. The method does not require expensive reference summaries and needs only a small number of preferences to align the generated summaries with human preferences. Code available at https://github.com/Haruhi07/PBRL-TLS.

This paper explores a novel technique that can aid firms in ascertaining a customer’s risk profile for the purpose of safeguarding them from unsuitable financial products. This falls under the purview of Know Your Customer (KYC), and a significant amount of regulation binds firms to this standard, including the Financial Conduct Authority (FCA) handbook Section 5.2. We introduce a methodology for computing a customer’s risk score by converting their transactional data into a heatmap image, then extracting complex geometric features that are indicative of impulsive spending. This heatmap analysis provides an interpretable approach to analysing spending patterns. The model developed by this study achieved an F1 score of 94.6% when classifying these features, far outperforming alternative configurations. Our experiments used a transactional dataset produced by Lloyds Banking Group, a major UK retail bank, via agent-based modelling (ABM). This data was computer generated and at no point was real transactional data shared. This study shows that a combination of ABM and artificial intelligence techniques can be used to aid firms in adhering to financial regulation.

Abstract Recent work in natural language processing (NLP) has yielded appealing results from scaling model parameters and training data; however, using only scale to improve performance means that resource consumption also grows. Such resources include data, time, storage, or energy, all of which are naturally limited and unevenly distributed. This motivates research into efficient methods that require fewer resources to achieve similar results. This survey synthesizes and relates current methods and findings in efficient NLP. We aim to provide both guidance for conducting NLP under limited resources, and point towards promising research directions for developing more efficient methods.

Neural models for response generation produce responses that are semantically plausible but not necessarily factually consistent with facts describing the speaker's persona.These models are trained with fully supervised learning where the objective function barely captures factual consistency.We propose to finetune these models by reinforcement learning and an efficient reward function that explicitly captures the consistency between a response and persona facts as well as semantic plausibility 1 .Our automatic and human evaluations on the PersonaChat corpus confirm that our approach increases the rate of responses that are factually consistent with persona facts over its supervised counterpart while retaining the language quality of responses.

Current methods for sequence tagging depend on large quantities of domain-specific training data, limiting their use in new, user-defined tasks with few or no annotations. While crowdsourcing can be a cheap source of labels, it often introduces errors that degrade the performance of models trained on such crowdsourced data. Another solution is to use transfer learning to tackle low resource sequence labelling, but current approaches rely heavily on similar high resource datasets in different languages. In this paper, we propose a domain adaptation method using Bayesian sequence combination to exploit pre-trained models and unreliable crowdsourced data that does not require high resource data in a different language. Our method boosts performance by learning the relationship between each labeller and the target task and trains a sequence labeller on the target domain with little or no gold-standard data. We apply our approach to labelling diagnostic classes in medical and educational case studies, showing that the model achieves strong performance though zero-shot transfer learning and is more effective than alternative ensemble methods. Using NER and information extraction tasks, we show how our approach can train a model directly from crowdsourced labels, outperforming pipeline approaches that first aggregate the crowdsourced data, then train on the aggregated labels.

Like any other science, research in natural language processing (NLP) depends on the ability to draw correct conclusions from experiments. A key tool for this is statistical significance testing: We use it to judge whether a result provides meaningful, generalizable findings or should be taken with a pinch of salt. When comparing new methods against others, performance metrics often differ by only small amounts, so researchers turn to significance tests to show that improved models are genuinely better. Unfortunately, this reasoning often fails because we choose inappropriate significance tests or carry them out incorrectly, making their outcomes meaningless. Or, the test we use may fail to indicate a significant result when a more appropriate test would find one. NLP researchers must avoid these pitfalls to ensure that their evaluations are sound and ultimately avoid wasting time and money through incorrect conclusions.This book guides NLP researchers through the whole process of significance testing, making it easy to select the right kind of test by matching canonical NLP tasks to specific significance testing procedures. As well as being a handbook for researchers, the book provides theoretical background on significance testing, includes new methods that solve problems with significance tests in the world of deep learning and multidataset benchmarks, and describes the open research problems of significance testing for NLP.The book focuses on the task of comparing one algorithm with another. At the core of this is the p-value, the probability that a difference at least as extreme as the one we observed could occur by chance. If the p-value falls below a predetermined threshold, the result is declared significant. Leaving aside the fundamental limitation of turning the validity of results into a binary question with an arbitrary threshold, to be a valid statistical significance test, the p-value must be computed in the right way. The book describes the two crucial properties of an appropriate significance test: The test must be both valid and powerful. Validity refers to the avoidance of type 1 errors, in which the result is incorrectly declared significant. Common mistakes that lead to type 1 errors include deploying tests that make incorrect assumptions, such as independence between data points. The power of a test refers to its ability to detect a significant result and therefore to avoid type 2 errors. Here, knowledge of the data and experiment must be used to choose a test that makes the correct assumptions. There is a trade-off between validity and power, but for the most common NLP tasks (language modeling, sequence labeling, translation, etc.), there are clear choices of tests that provide a good balance.Beginning with a detailed background on significance testing, the book then shows the reader how to carry out tests for specific NLP tasks. There is a mix of styles, with the first four chapters providing reference material that will be extremely useful to both new and experienced researchers. Here, it is easy to find the material related to a given NLP task. The next two chapters discuss more recent research into the application of significance tests to deep neural networks and for testing across multiple datasets. Alongside open research questions, these later chapters provide clear guidelines on how to apply the proposed methods. It is this mix of background material and reference guidelines that I believe makes this book so compelling and nicely self-contained.The introduction in Chapter 1 motivates the need for a comprehensive textbook and outlines challenges that the later chapters address more deeply. The theoretical background material begins in Chapter 2, which introduces core concepts, including hypothesis testing, type 1 and type 2 errors, validity and power, and p-values. The reader does not need to have any prior knowledge of statistical significance tests to follow this part. However, experienced readers could still benefit from reading this chapter, as concepts such as p-values are widely misunderstood and misused (Amrhein, Greenland, and McShane 2019).The significance tests themselves are introduced in Chapter 3, categorized into parametric and nonparametric tests. The chapter begins with the intuitively simple paired z-test, then builds up to more commonly-applied techniques, showing the connections and assumptions that each test makes. Step-by-step algorithms help the reader to implement each test. Although the chapter does cite uses of tests in NLP research, the main purpose is to present the theory behind each test and point out their differences.Chapter 4 provides perhaps the most handy part of the book for reference: a correspondence between common NLP tasks and statistical tests. Each task is discussed in terms of the evaluation metrics used, then a decision tree is introduced to guide the reader toward a choice between a parametric test, bootstrap or randomization test, or sampling-free nonparametric test. Section 4.3 then links each NLP evaluation measure to a specific significance test, presenting a large table that helps readers identify which test they need for a specific task. Particular considerations for each task are also pointed out to provide more detail about the appropriate options. The final part of this chapter describes the issue of p-hacking, in which dataset sizes are increased until a significance threshold is reached without consideration for biases in the data (discussed, for example, in Hofmann [2015]). The chapter proposes a simple solution to ensure robust significance testing with large datasets.Where Chapter 4 presents well-established methods, Chapter 5 introduces the current research question of how best to apply statistical significance testing to deep learning. Non-convex loss functions, stochastic optimization, random initialization, and a multitude of hyperparameters limit the conclusions we can draw from a single test run of a deep neural network (DNN). This chapter, which is based on the authors’ ACL paper (Dror, Shlomov, and Reichart 2019), explains how the comparison process can be overhauled to provide more meaningful evaluations. Beginning by explaining the difficulties of evaluating DNNs, the chapter then introduces criteria for a comparison framework, then discusses the limitations of current methods. Reimers and Gurevych (2018) have previously tackled this problem, but their approach has limited power and does not provide a confidence score. Other works, such as Clark et al. (2011), compare DNNs using a collection of statistics, such as the mean or standard deviation of performance across runs. This book shows how such an approach violates the assumptions of the significance tests. The authors propose almost stochastic dominance as the basis for a better alternative. The chapter explains how to use the proposed method, evaluates it in an empirical case study, and finally analyzes the errors made by each testing approach.Large NLP models are often tested across a range of datasets, which presents another problem for standard significance testing. Chapter 6 discusses the challenges of assessing two questions: (1) On how many datasets does algorithm A outperform algorithm B? (2) On which datasets does A outperform B? Applying standard significance tests individually to each dataset and counting the number of significant results is likely to overestimate the total number of significant results, as this chapter explains. The authors then present a new framework for replicability analysis, based on partial conjunction testing, and discuss two variants (Bonferroni and Fisher) for when the datasets are independent or dependent. They introduce a method based on Benjamini and Heller (2008) to count the number of datasets where one method outperforms another, then show how to use the Holm procedure (Holm 1979) to identify which datasets these are. Chapter 6 provides a lot of detailed background on the proposed replicability analysis framework, and the later sections again link the process to specific NLP case studies, and step-by-step summaries help the reader to apply the methodology. Extensive empirical results illustrate the very substantial differences in outcomes between the proposed approach and standard procedures.The final two chapters present open problems and conclude, showing that the topic has many interesting research questions of its own, such as problems when performing cross-validation, and the limited statistical power of replicability analysis.Overall, I highly recommend this book to a wide range of NLP researchers, from new students to seasoned experts who wish to ensure that they compare methods effectively. The book is excellent as both an introduction to the topic of significance testing and as a reference to use when evaluating your results. For anyone with further interest in the topic, it also points the way to future work. If one could level any criticism at this book at all, it is that it does not deeply discuss the basic flaws of significance testing or what the alternatives might be. For now, though, significance testing is an integral part of NLP research and this book provides a great resource for researchers who wish to perform it correctly and painlessly.

Abstract We propose a scalable Bayesian preference learning method for jointly predicting the preferences of individuals as well as the consensus of a crowd from pairwise labels. Peoples’ opinions often differ greatly, making it difficult to predict their preferences from small amounts of personal data. Individual biases also make it harder to infer the consensus of a crowd when there are few labels per item. We address these challenges by combining matrix factorisation with Gaussian processes, using a Bayesian approach to account for uncertainty arising from noisy and sparse data. Our method exploits input features, such as text embeddings and user metadata, to predict preferences for new items and users that are not in the training set. As previous solutions based on Gaussian processes do not scale to large numbers of users, items or pairwise labels, we propose a stochastic variational inference approach that limits computational and memory costs. Our experiments on a recommendation task show that our method is competitive with previous approaches despite our scalable inference approximation. We demonstrate the method’s scalability on a natural language processing task with thousands of users and items, and show improvements over the state of the art on this task. We make our software publicly available for future work ( https://github.com/UKPLab/tacl2018-preference-convincing/tree/crowdGPPL ).

For many NLP applications, such as question answering and summarization, the goal is to select the best solution from a large space of candidates to meet a particular user’s needs. To address the lack of user or task-specific training data, we propose an interactive text ranking approach that actively selects pairs of candidates, from which the user selects the best. Unlike previous strategies, which attempt to learn a ranking across the whole candidate space, our method uses Bayesian optimization to focus the user’s labeling effort on high quality candidates and integrate prior knowledge to cope better with small data scenarios. We apply our method to community question answering (cQA) and extractive multidocument summarization, finding that it significantly outperforms existing interactive approaches. We also show that the ranking function learned by our method is an effective reward function for reinforcement learning, which improves the state of the art for interactive summarization.

The inability to quantify key aspects of creative language is a frequent obstacle to natural language understanding.To address this, we introduce novel tasks for evaluating the creativeness of language-namely, scoring and ranking text by humorousness and metaphor novelty.To sidestep the difficulty of assigning discrete labels or numeric scores, we learn from pairwise comparisons between texts.We introduce a Bayesian approach for predicting humorousness and metaphor novelty using Gaussian process preference learning (GPPL), which achieves a Spearman's ρ of 0.56 against gold using word embeddings and linguistic features.Our experiments show that given sparse, crowdsourced annotation data, ranking using GPPL outperforms best-worst scaling.We release a new dataset for evaluating humour containing 28,210 pairwise comparisons of 4030 texts, and make our software freely available.1 https://github.com/ukplab/ acl2019

Edwin Simpson, Iryna Gurevych. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

We introduce a scalable Bayesian preference learning method for identifying convincing arguments in the absence of gold-standard ratings or rankings. In contrast to previous work, we avoid the need for separate methods to perform quality control on training data, predict rankings and perform pairwise classification. Bayesian approaches are an effective solution when faced with sparse or noisy training data, but have not previously been used to identify convincing arguments. One issue is scalability, which we address by developing a stochastic variational inference method for Gaussian process (GP) preference learning. We show how our method can be applied to predict argument convincingness from crowdsourced data, outperforming the previous state-of-the-art, particularly when trained with small amounts of unreliable data. We demonstrate how the Bayesian approach enables more effective active learning, thereby reducing the amount of data required to identify convincing arguments for new users and domains. While word embeddings are principally used with neural networks, our results show that word embeddings in combination with linguistic features also benefit GPs when predicting argument convincingness.

Social media has led to the democratisation of opinion sharing. A wealth of information about public opinions, current events, and authors' insights into specific topics can be gained by understanding the text written by users. However, there is a wide variation in the language used by different authors in different contexts on the web. This diversity in language makes interpretation an extremely challenging task. Crowdsourcing presents an opportunity to interpret the sentiment, or topic, of free-text. However, the subjectivity and bias of human interpreters raise challenges in inferring the semantics expressed by the text. To overcome this problem, we present a novel Bayesian approach to language understanding that relies on aggregated crowdsourced judgements. Our model encodes the relationships between labels and text features in documents, such as tweets, web articles, and blog posts, accounting for the varying reliability of human labellers. It allows inference of annotations that scales to arbitrarily large pools of documents. Our evaluation using two challenging crowdsourcing datasets shows that by efficiently exploiting language models learnt from aggregated crowdsourced labels, we can provide up to 25% improved classifications when only a small portion, less than 4% of documents has been labelled. Compared to the six state-of-the-art methods, we reduce by up to 67% the number of crowd responses required to achieve comparable accuracy. Our method was a joint winner of the CrowdFlower - CrowdScale 2013 Shared Task challenge at the conference on Human Computation and Crowdsourcing (HCOMP 2013).