India

Several multilingual benchmark datasets have been developed in a semi-automatic manner in the recent past to measure progress and understand the state-of-the-art in the multilingual capabilities of Large Language Models (LLM). However, there is not a lot of attention paid to the quality of the datasets themselves, despite the existence of previous work in identifying errors in even fully human-annotated test sets. In this paper, we manually analyze recent multilingual evaluation sets in two languages -- French and Telugu, identifying several errors in the datasets during the process. We compare the performance difference across several LLMs with the original and revised versions of the datasets and identify large differences (almost 10% in some cases) in both languages. Based on these results, we argue that test sets should not be considered immutable and should be revisited, checked for correctness, and potentially versioned. We end with some recommendations for both the dataset creators as well as consumers on addressing the dataset quality issues.

IJCNLP-AACL 2025

Test Set Quality in Multilingual LLM Evaluation

dataset quality

evaluation

multilingual

# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, three amazing keynote events and two new initiatives of invited talks: Spotlight Talks for Young Rising Stars (STIRS) and The Next Big Idea Talks. Posters (including Findings of ACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop will have an oral session and a poster session as part of Poster Session 1. The program also features eight Tutorials and 28 Workshops. 

 
We wish you a wonderful conference! 
[**The ACL 2022 Organizing Committee**](https://www.2022.aclweb.org/organisers)
 
[**Conference Handbook**](https://drive.google.com/file/d/1_BUCMfhMVrjG9E2e71aHdHeE28KSje0l/view?usp=sharing) 
[**Mini Handbook**](https://drive.google.com/file/d/1qlBKl0wzmlVF1oCeMQl3BahLd9nLP5Ce/view?usp=sharing) 
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ACL 2022

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

workshop paper

### Welcome to IJCNLP-AACL 2025! 
 It is a great honor to host this joint conference in Mumbai, India, from December 20 to 24, 2025. The joint conferences of IJCNLP and AACL are organized with alternating leadership in the Asia-Pacific region. The event is run by the Asian Federation of Natural Language Processing (AFNLP) in odd years, and by AACL in even years, while it is organized solely by ACL when the annual ACL meeting is held in the region. This year, the conference is primarily organized by AFNLP. 
*Kentaro Inui
MBZUAI, UAE
General Chair, IJCNLP-AACL 2025* 
Read full message and download the Conference Handbook [**here**](https://drive.google.com/file/d/1UTwxkAqSqI-GAoJC3wE1zZt5VP1Y8GX0/view?usp=sharing).

The 14th IJCNLP & 4th AACL will be held in Mumbai, India from December 20th to December 24th, 2025.

This paper introduces Indian Financial Narrative Inference Tasks and Evaluations (InFiNITE), a comprehensive framework for analyzing India's financial narratives through three novel inference tasks. Firstly, we present multi-modal earnings call analysis by integrating transcripts, presentation visuals, and market indicators via the Multi-Modal Indian Earnings Calls (MiMIC) dataset, enabling holistic prediction of post-call stock movements. Secondly, our Budget-Assisted Sectoral Impact Ranking (BASIR) dataset aids in systematically decoding government fiscal narratives by classifying budget excerpts into 81 economic sectors and evaluating their post-announcement equity performance. Thirdly, we introduce Bharat IPO Rating (BIR) datasets to redefine Initial Public Offering (IPO) evaluation through prospectus analysis, classifying potential investments into four recommendation categories (Apply, May Apply, Neutral, Avoid). By unifying textual, visual, and quantitative modalities across corporate, governmental, and public investment domains, InFiNITE addresses critical gaps in Indian financial narrative analysis. The open source data sets of the framework, including earnings calls, union budgets, and IPO prospectuses, establish benchmark resources specific to India for computational economic research under permissive licenses. For investors, InFiNITE enables data-driven identification of capital allocation opportunities and IPO risks, while policymakers gain structured insights to assess Indian fiscal communication impacts. By releasing these datasets publicly, we aim to facilitate research in computational economics and financial text analysis, particularly for the Indian market.

InFiNITE (∞): Indian Financial Narrative Inference Tasks & Evaluations

LLM (large language model) users of hosted providers commonly notice that outputs can vary for the same inputs under settings expected to be deterministic. While it is difficult to get exact statistics, recent reports on specialty news sites and discussion boards suggest that among users in all communities, the majority of LLM usage today is through cloud-based APIs. Yet the questions of how pervasive non- determinism is, and how much it affects perfor- mance results, have not to our knowledge been systematically investigated. We apply five API- based LLMs configured to be deterministic to eight diverse tasks across 10 runs. Experiments reveal accuracy variations of up to 15% across runs, with a gap of up to 70% between best pos- sible performance and worst possible perfor- mance. No LLM consistently delivers the same outputs or accuracies, regardless of task. We speculate about the sources of non-determinism such as input buffer packing across multiple jobs. To better quantify our observations, we introduce metrics focused on quantifying de- terminism, TARr@N for the total agreement rate at N runs over raw output, and TARa@N for total agreement rate of parsed-out answers. Our code and data will be publicly available at https://github.com/Anonymous.

Non-Determinism of “Deterministic” LLM System Settings in Hosted Environments

Large language models (LLMs) are increasingly used to assist scientists across diverse workflows. A key challenge is generating high-quality figures from textual descriptions, often represented as TikZ programs that can be rendered as scientific images. Prior research has proposed a variety of datasets and modeling approaches for this task. However, existing datasets for Text-to-TikZ are too small and noisy to capture the complexity of TikZ, causing mismatches between text and rendered figures. Moreover, prior approaches rely solely on supervised fine-tuning (SFT), which does not expose the model to the rendered semantics of the figure, often resulting in errors such as looping, irrelevant content, and incorrect spatial relations. To address these issues, we construct DaTikZ-V4, a dataset more than four times larger and substantially higher in quality than DaTikZ-V3, enriched with LLM-generated figure descriptions. Using this dataset, we train TikZilla, a family of small open-source Qwen models (3B and 8B) with a two-stage pipeline of SFT followed by reinforcement learning (RL). For RL, we leverage an image encoder trained via inverse graphics to provide semantically faithful reward signals. Extensive human evaluations with over 1,000 judgments show that TikZilla improves by 1.5-2 points over its base models on a 5-point scale, surpasses GPT-4o by 0.5 points, and matches GPT-5 in the image-based evaluation, while operating at much smaller model sizes. Code, data, and models will be made available.

TikZilla: Scaling Text-to-TikZ with High-Quality Data and Reinforcement Learning

Large Language Models (LLMs) show strong reasoning and text generation capabilities, prompting their use in scientific literature analysis, including novelty assessment. While evaluating novelty of scientific papers is crucial for peer review, it requires extensive knowledge of related work, something not all reviewers have.While recent work on LLM-assisted scientific literature analysis supports literature comparison, existing approaches offer limited transparency and lack mechanisms for result traceability via an information retrieval module. To address this gap, we introduce GraphMind, an easy-to-use interactive web tool designed to assist users in evaluating the novelty of scientific papers or drafted ideas. Specially, GraphMind enables users to capture the main structure of a scientific paper, explore related ideas through various perspectives, and assess novelty via providing verifiable contextual insights. GraphMind enables users to annotate key elements of a paper, explore related papers through various relationships, and assess novelty with contextual insight. This tool integrates external APIs such as arXiv and Semantic Scholar with LLMs to support annotation, extraction, retrieval and classification of papers. This combination provides users with a rich, structured view of a scientific idea's core contributions and its connections to existing work. GraphMind is available at https://oyarsa.github.io/graphmind and a demonstration video at https://youtu.be/wKbjQpSvwJg

GraphMind: Interactive Novelty Assessment System for Accelerating Scientific Discovery

Retrieval-Augmented Generation (RAG) has emerged as an effective strategy to ground Large Language Models (LLMs) with reliable, real-time information. This paper investigates the trade-off between cost and performance by evaluating 13 LLMs within a RAG pipeline for the Question Answering (Q&A) task under noisy retrieval conditions. We assess four extractive and nine generative models—spanning both open- and closed-source ones of varying sizes—on a journalistic benchmark specifically designed for RAG. By systematically varying the level of noise injected into the retrieved context, we analyze not only which models perform best, but also their robustness to noisy input. Results show that large open-source generative models (approx. 70B parameters) achieve performance and noise tolerance on par with top-tier closed-source models. However, their computational demands limit their practicality in resource-constrained settings. In contrast, medium-sized open-source models (approx. 7B parameters) emerge as a compelling compromise, balancing efficiency, robustness, and accessibility.

A Comprehensive Evaluation of Large Language Models for Retrieval-Augmented Generation under Noisy Conditions

This paper presents a comprehensive study on the multilingual adaptability of large language models (LLMs), with a focus on the interplay between training strategies and prompt design. Using Thai as a case study, we examine: (RQ1) the extent to which pre-trained models (Base) can adapt to another language through additional fine-tuning; (RQ2) how continual pre-training (CPT) compares to multilingual pre-training (MLLM) in terms of performance on downstream tasks; and (RQ3) how language variation within different components of a structured prompt--task instruction, context input, and output instruction--influences task performance in cross-lingual settings. Our findings reveal that CPT proves to be a promising strategy for enhancing model performance in languages other than English like Thai in monolingual settings, particularly for models that initially lack strong linguistic capabilities. Its effectiveness, however, is highly task-dependent and varies based on the base model's initial proficiency. In cross-lingual scenarios, MLLMs exhibit superior robustness compared to Base and CPT models, which are more susceptible to context-output language mismatches. Considering the high cost of training multilingual models from scratch, MLLMs remain a critical component for downstream tasks in multilingual settings due to their strong cross-lingual performance.

Language Confusion and Multilingual Performance: A Case Study of Thai-Adapted Large Language Models

Hallucinations of vision-language models (VLMs), which are misalignments between visual content and generated text, undermine the reliability of VLMs. One common approach for detecting them employs the same VLM, or a different one, to assess generated outputs. This process is computationally intensive and increases model latency. In this paper, we explore an efficient on-the-fly method for hallucination detection by training traditional ML models over signals based on the VLM's next-token probabilities (NTPs). NTPs provide a direct quantification of model uncertainty. We hypothesize that high uncertainty (i.e., a low NTP value) is strongly associated with hallucinations. To test this, we introduce a dataset of 1,400 human-annotated statements derived from VLM-generated content, each labeled as hallucinated or not, and use it to test our NTP-based lightweight method. Our results demonstrate that NTP-based features are valuable predictors of hallucinations, enabling fast and simple ML models to achieve performance comparable to that of strong VLMs. Furthermore, augmenting these NTPs with linguistic NTPs, computed by feeding only the generated text back into the VLM, enhances hallucination detection performance. Finally, integrating hallucination prediction scores from VLMs into the NTP-based models led to better performance than using either VLMs or NTPs alone. We hope this study paves the way for simple, lightweight solutions that enhance the reliability of VLMs. All data is publicly available at https://huggingface.co/datasets/wrom/Language-Vision-Hallucinations.

Leveraging NTPs for Efficient Hallucination Detection in VLMs

In this paper, we explore the potential of Open-source Small Language Models (OSLMs) for localizing hallucinations related to factual accuracy. We first present Lucifer, a dataset designed to enable proper and consistent evaluation of LMs, composed of an automatically constructed portion and a manually curated subset intended for qualitative analysis. We then assess the performance of five OSLMs using four carefully designed prompts. Results are evaluated either individually or merged through a voting-based merging approach. While our results demonstrate that the merging method yields promising performance even with smaller models, our manually curated dataset highlights the inherent difficulty of the task, underscoring the need for further research.

Better Together: Towards Localizing Fact-Related Hallucinations using Open Small Language Models

Chain-of-Thought (CoT) ‘reasoning’ promises to enhance the performance and transparency of Large Language Models (LLMs). Models, such as Deepseek R1, are trained via reinforcement learning to automatically generate CoT explanations in their outputs. Their faithfulness, i.e. how well the explanations actually reflect their internal reasoning process, has been called into doubt by recent studies (Chen et al., 2025a; Chua and Evans, 2025). This paper extends previous work by probing Deepseek R1 with 445 logical puzzles under zero- and few-shot settings. We find that whilst the model explicitly acknowledges a strong harmful hint in 94.6% of cases, it reports less than 2% of helpful hints. Further analysis reveals implicit unfaithfulness as the model significantly reduces answer-rechecking behaviour for helpful hints (p<0.01) despite rarely mentioning them in its CoT, demonstrating a discrepancy between its reported and actual decision process. In line with prior reports for GPT, Claude, Gemini and other models, our results for DeepSeek raise concerns about the use of CoT as an explainability technique.

Examining the Faithfulness of Deepseek R1’s Chain-of-Thought Reasoning

Retrieval-Augmented Generation (RAG) enhances language models by grounding outputs in retrieved, relevant documents, effectively addressing key limitations such as hallucinations, updated knowledge, and restricted domain expertise. We propose CREAM-RAG, which stabilizes self-reward signals by applying consistency regularization, reducing hallucinations, and improving factual accuracy. Our framework integrates retrieval, DPO-based self-reward reinforcement learning, and consistency loss into a unified system for more reliable output. Experimental results demonstrated significant performance improvements on the LLaMA 2 7B model. It improved the results on average by 35.04% compared to the base model, suggesting that CREAM-RAG successfully enhanced semantic reasoning and decreased hallucinations.

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