Wisdom of the Crowds in Forecasting: Forecast Summarization for Supporting Future Event Prediction

Future Event Prediction (FEP) is an essential activity whose demand and application range across multiple domains. While traditional methods like simulations, predictive and time-series forecasting have demonstrated promising outcomes, their application in forecasting complex events is not entirely reliable due to the inability of numerical data to accurately capture the semantic information related to events. One forecasting way is to gather and aggregate collective opinions on the future to make predictions as cumulative perspectives carry the potential to help estimating the likelihood of upcoming events. In this work, we organize the existing research and frameworks that aim to support future event prediction based on crowd wisdom through aggregating individual forecasts. We discuss the challenges involved, available datasets, as well as the scope of improvement and future research directions for this task. We also introduce a novel data model to represent individual forecast statements.

Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation

Retrieval, re-ranking, and retrieval-augmented generation (RAG) are critical components of modern natural language processing (NLP) applications in information retrieval, question answering, and knowledge-based text generation. However, existing solutions are often fragmented, lacking a unified framework that easily integrates these essential processes. The absence of a standardized implementation, coupled with the complexity of retrieval and re-ranking workflows, makes it challenging for researchers to compare and evaluate different approaches in a consistent environment. While existing toolkits such as Rerankers and RankLLM provide general-purpose reranking pipelines, they often lack the flexibility required for fine-grained experimentation and benchmarking. In response to these challenges, we introduce \textbf{Rankify}, a powerful and modular open-source toolkit designed to unify retrieval, re-ranking, and RAG within a cohesive framework. Rankify supports a wide range of retrieval techniques, including dense and sparse retrievers, while incorporating state-of-the-art re-ranking models to enhance retrieval quality. Additionally, Rankify includes a collection of pre-retrieved datasets to facilitate benchmarking, available at Huggingface (https://huggingface.co/datasets/abdoelsayed/reranking-datasets). To encourage adoption and ease of integration, we provide comprehensive documentation (http://rankify.readthedocs.io/), an open-source implementation on GitHub(https://github.com/DataScienceUIBK/rankify), and a PyPI package for effortless installation(https://pypi.org/project/rankify/). By providing a unified and lightweight framework, Rankify allows researchers and practitioners to advance retrieval and re-ranking methodologies while ensuring consistency, scalability, and ease of use.

HintEval: A Comprehensive Framework for Hint Generation and Evaluation for Questions

Large Language Models (LLMs) are transforming how people find information, and many users turn nowadays to chatbots to obtain answers to their questions. Despite the instant access to abundant information that LLMs offer, it is still important to promote critical thinking and problem-solving skills. Automatic hint generation is a new task that aims to support humans in answering questions by themselves by creating hints that guide users toward answers without directly revealing them. In this context, hint evaluation focuses on measuring the quality of hints, helping to improve the hint generation approaches. However, resources for hint research are currently spanning different formats and datasets, while the evaluation tools are missing or incompatible, making it hard for researchers to compare and test their models. To overcome these challenges, we introduce HintEval, a Python library that makes it easy to access diverse datasets and provides multiple approaches to generate and evaluate hints. HintEval aggregates the scattered resources into a single toolkit that supports a range of research goals and enables a clear, multi-faceted, and reliable evaluation. The proposed library also includes detailed online documentation, helping users quickly explore its features and get started. By reducing barriers to entry and encouraging consistent evaluation practices, HintEval offers a major step forward for facilitating hint generation and analysis research within the NLP/IR community.

ASRank: Zero-Shot Re-Ranking with Answer Scent for Document Retrieval

Retrieval-Augmented Generation (RAG) models have drawn considerable attention in modern open-domain question answering. The effectiveness of RAG depends on the quality of the top retrieved documents. However, conventional retrieval methods sometimes fail to rank the most relevant documents at the top. In this paper, we introduce ASRank, a new re-ranking method based on scoring retrieved documents using zero-shot answer scent which relies on a pre-trained large language model to compute the likelihood of the document-derived answers aligning with the answer scent. Our approach demonstrates marked improvements across several datasets, including NQ, TriviaQA, WebQA, ArchivalQA, HotpotQA, and Entity Questions. Notably, ASRank increases Top-1 retrieval accuracy on NQ from $19.2\%$ to $46.5\%$ for MSS and $22.1\%$ to $47.3\%$ for BM25. It also shows strong retrieval performance on several datasets compared to state-of-the-art methods (47.3 Top-1 by ASRank vs 35.4 by UPR by BM25).

Analyzing the Role of Context in Forecasting with Large Language Models

This study evaluates the forecasting performance of recent language models (LLMs) on binary forecasting questions. We first introduce a novel dataset of over 600 binary forecasting questions, augmented with related news articles and their concise question-related summaries. We then explore the impact of input prompts with varying level of context on forecasting performance. The results indicate that incorporating news articles significantly improves performance, while using few-shot examples leads to a decline in accuracy. We find that larger models consistently outperform smaller models, highlighting the potential of LLMs in enhancing automated forecasting.

Navigating Tomorrow: Reliably Assessing Large Language Models Performance on Future Event Prediction

Predicting future events is an important activity with applications across multiple fields and domains. For example, the capacity to foresee stock market trends, natural disasters, business developments, or political events can facilitate early preventive measures and uncover new opportunities. Multiple diverse computational methods for attempting future predictions, including predictive analysis, time series forecasting, and simulations have been proposed. This study evaluates the performance of several large language models (LLMs) in supporting future prediction tasks, an under-explored domain. We assess the models across three scenarios: Affirmative vs. Likelihood questioning, Reasoning, and Counterfactual analysis. For this, we create a dataset1 by finding and categorizing news articles based on entity type and its popularity. We gather news articles before and after the LLMs training cutoff date in order to thoroughly test and compare model performance. Our research highlights LLMs potential and limitations in predictive modeling, providing a foundation for future improvements.

Using Large Language Models in Automatic Hint Ranking and Generation Tasks

The use of Large Language Models (LLMs) has increased significantly recently, with individuals frequently interacting with chatbots to receive answers to a wide range of questions. In an era where information is readily accessible, it is crucial to stimulate and preserve human cognitive abilities and maintain strong reasoning skills. This paper addresses such challenges by promoting the use of hints as an alternative or a supplement to direct answers. We first introduce a manually constructed hint dataset, WIKIHINT, which includes 5,000 hints created for 1,000 questions. We then finetune open-source LLMs such as LLaMA-3.1 for hint generation in answer-aware and answer-agnostic contexts. We assess the effectiveness of the hints with human participants who try to answer questions with and without the aid of hints. Additionally, we introduce a lightweight evaluation method, HINTRANK, to evaluate and rank hints in both answer-aware and answer-agnostic settings. Our findings show that (a) the dataset helps generate more effective hints, (b) including answer information along with questions generally improves hint quality, and (c) encoder-based models perform better than decoder-based models in hint ranking.

DynRank: Improving Passage Retrieval with Dynamic Zero-Shot Prompting Based on Question Classification

This paper presents DynRank, a novel framework for enhancing passage retrieval in open-domain question-answering systems through dynamic zero-shot question classification. Traditional approaches rely on static prompts and pre-defined templates, which may limit model adaptability across different questions and contexts. In contrast, DynRank introduces a dynamic prompting mechanism, leveraging a pre-trained question classification model that categorizes questions into fine-grained types. Based on these classifications, contextually relevant prompts are generated, enabling more effective passage retrieval. We integrate DynRank into existing retrieval frameworks and conduct extensive experiments on multiple QA benchmark datasets.

Listen to the Patient: Enhancing Medical Dialogue Generation with Patient Hallucination Detection and Mitigation

Medical dialogue systems aim to provide medical services through patient-agent conversations. Previous methods typically regard patients as ideal users, focusing mainly on common challenges in dialogue systems, while neglecting the potential biases or misconceptions that might be introduced by real patients, who are typically non-experts. This study investigates the discrepancy between patients' expressions during medical consultations and their actual health conditions, defined as patient hallucination. Such phenomena often arise from patients' lack of knowledge and comprehension, concerns, and anxieties, resulting in the transmission of inaccurate or wrong information during consultations. To address this issue, we propose MedPH, a Medical dialogue generation method for mitigating the problem of Patient Hallucinations designed to detect and cope with hallucinations. MedPH incorporates a detection method that utilizes one-dimensional structural entropy over a temporal dialogue entity graph, and a mitigation strategy based on hallucination-related information to guide patients in expressing their actual conditions. Experimental results indicate the high effectiveness of MedPH when compared to existing approaches in both medical entity prediction and response generation tasks, while also demonstrating its effectiveness in mitigating hallucinations within interactive scenarios.

LLMTemporalComparator: A Tool for Analysing Differences in Temporal Adaptations of Large Language Models

This study addresses the challenges of analyzing temporal discrepancies in large language models (LLMs) trained on data from different time periods. To facilitate the automatic exploration of these differences, we propose a novel system that compares in a systematic way the outputs of two LLM versions based on user-defined queries. The system first generates a hierarchical topic structure rooted in a user-specified keyword, allowing for an organized comparison of topical categories. Subsequently, it evaluates the generated text by both LLMs to identify differences in vocabulary, information presentation, and underlying themes. This fully automated approach not only streamlines the identification of shifts in public opinion and cultural norms but also enhances our understanding of the adaptability and robustness of machine learning applications in response to temporal changes. By fostering research in continual model adaptation and comparative summarization, this work contributes to the development of more transparent machine learning models capable of capturing the nuances of evolving societal contexts.