India

This paper introduces Indian Financial Narrative Inference Tasks and Evaluations (InFiNITE), a comprehensive framework for analyzing India&#39;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.

IJCNLP-AACL 2025

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

nlp datasets

language resources

corpus creation

benchmarking

evaluation

## Welcome to EMNLP 2024! 
We are excited to welcome you to one of the most prominent conferences in the field of Natural Language Processing. This year, EMNLP 2024 is being held in a hybrid format,
offering both virtual and in-person participation in beautiful Miami. Due to a record-breaking number of submissions, we've expanded the total number of accepted papers to accommodate more cutting-edge research from around the globe.
### [Conference Handbook](https://drive.google.com/file/d/1WPROgxjLAC96AJL7Ugy0tEnYm7dkrbHt/view?usp=sharing)

You are required to register for this event. **Please register [here](https://2024.emnlp.org/registration/).** The EMNLP 2024 event page on Underline will be open to public one week prior to the event.

Please register!

EMNLP 2024

EMNLP 2024 will take place in Miami, Florida from Nov 12th to Nov 16th, 2024, at the Hyatt Regency Miami Hote and on Underline for remote participants.

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

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.

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.

CREAM-RAG: Enhanced Retrieval Augmented Generation to Limit Hallucination through Consistency-based Self-RAG

LLMs are proving to be adept at machine translation although due to their generative nature they may at times overgenerate in various ways. These overgenerations are different from the neurobabble seen in NMT and range from LLM self-explanations, to risky confabulations, to appropriate explanations, where the LLM is able to act as a human translator would- enabling greater comprehension for the target audience. Detecting and determining the exact nature of the overgenerations is a challenging task. We detail different strategies we have explored for our work in a commercial setting, and present our results.

Downloads

Next from IJCNLP-AACL 2025

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

Similar lecture

Numerical Claim Detection in Finance: A New Financial Dataset, Weak-Supervision Model, and Market Analysis