Topic
LLMs & Text
Large language models, agents, sentiment and text as data in finance.
- Papers featured
- 584
- Last 12 months
- 25
- Cited 100+
- 103
- Top venue
- Machine learning
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Featured papers in this topic with the most citations today.
- 7 Feb 20249,718cites
DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
Advancing Math Reasoning in Language Models: DeepSeekMath7B is a new language model that uses web data and Group Relative Policy Optimization for advanced mathematical reasoning, scoring high on the MATH benchmark.
Machine learning
- 16 Oct 20234,011cites
Mistral 7B
Superior Language Model: Mistral 7B v0.1 is a language model with 7 billion parameters that excels in reasoning, mathematics, and code generation, and has a version specifically designed to follow instructions.
Machine learning
- 7 Aug 20242,266cites
Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
The research investigates enhancing Large Language Models' (LLMs) performance using more test-time computation, suggesting a compute-optimal scaling strategy based on prompt difficulty.
Machine learning
- 24 Jul 20241,914cites
AWQ: Activation-aware Weight Quantization for On-Device LLM Compression and Acceleration
The study suggests Activation-aware Weight Quantization (AWQ), a hardware-friendly method for quantizing large language models that reduces error and improves performance on various benchmarks.
Machine learningIn GetMobile: Mobile Computing and Communications
- 12 Dec 20241,802cites
Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling
The paper presents InternVL 2.5, a sophisticated multimodal large language model that performs well on various benchmarks, exceeding 70% on the MMMU benchmark.
Machine learning
- 16 Oct 20231,558cites
MemGPT: Towards LLMs as Operating Systems
Extended Context in LLMs: MemGPT is a system that manages different memory levels, providing extended context within large language models' limited context windows, enhancing document analysis and multi-session chat performance.
Machine learningFeatured 2×
- 5 Feb 20251,521cites
s1: Simple test-time scaling
The research presents a method called budget forcing, which uses a small dataset to achieve test-time scaling and improved reasoning performance in language modeling, particularly in competition math questions.
Machine learningIn Conference on Empirical Methods in Natural Language Processing
- 22 May 20241,507cites
DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
MoE Language Model: DeepSeek-V2, a language model with 236B parameters, offers enhanced performance and cost efficiency compared to its predecessor, ranking high among open-source models.
Machine learning
- 16 Oct 20231,302cites
SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models
Hallucination Detection for LLMs: The paper presents SelfCheckGPT, a new approach for fact-checking black-box model responses without an external database, proving its superior ability to detect and rank factual and non-factual sentences.
Machine learningIn Conference on Empirical Methods in Natural Language ProcessingFeatured 2×
- 8 May 20241,012cites
A Simple and Effective Pruning Approach for Large Language Models
Wanda, a new method, efficiently prunes weights in Large Language Models without retraining, offering a more efficient approach to inducing sparsity in pretrained models.
Machine learningIn International Conference on Learning Representations
- 5 Feb 2025975cites
TÜLU 3: Pushing Frontiers in Open Language Model Post-Training
Open Language Model Post-Training: The Tulu 3 model, a top-tier post-trained language model, is introduced, outperforming other models and providing a detailed guide for its use and adaptation.
Machine learning
- 12 Jun 2024947cites
Simple and Effective Masked Diffusion Language Models
The performance of diffusion models in language modeling has been enhanced by using an effective training recipe and a simplified objective, setting a new standard among diffusion models.
Machine learningIn Neural Information Processing Systems
Latest
- 9 Oct 20264fanfare
Thin Evidence, Thick Priors: How Language Models Substitute Identity for Missing Financial Facts
When financial facts are withdrawn from prompts, identity attributes explain 96% of variation in equity allocation advice from a language model, replacing missing evidence.
arXiv
- 9 Oct 20263fanfare
MintEval: Do LLMs Implement the Trading Strategy You Asked For? A Behavioural-Equivalence Benchmark for Natural-Language-to-Strategy Code
Introduces a benchmark that compares LLM-generated trading code to reference strategies bar by bar on identical data, revealing silent failures in implementation despite passing functional tests.
arXiv
- 9 Oct 20262fanfare
Large Language Models and Information Frictions in Corporate Bonds
Shows that large-language-model scores of default risk from earnings calls predict bond rating migration one year ahead where analysts disagreed, capturing information not yet priced in spreads.
SSRN
- 9 Oct 20263fanfare
Geoeconomic Fear and Sovereign Spreads
The study constructs a geoeconomic fear index from news articles and finds that higher fear predicts wider emerging-market sovereign spreads, especially for speculative-grade issuers.
SSRN
- 2 Oct 20260cites
Resolving the Missing Financial Data Crisis: A Generative AI Pipeline for SEC 10-K Extraction
Large language models outperform traditional methods at extracting missing financial data from SEC filings, with accuracy improving as model size matches document complexity.
arXiv
- 2 Oct 20263fanfare
Transformer-based CoVaR: Systemic Risk in Textual Information
Integrating financial news embeddings from large language models with market data, the study improves systemic risk forecasts using conditional value-at-risk without requiring large datasets.
RePEc
- 2 Oct 20262fanfare
One advisor for the whole world? Cross-country evidence on financial advice from large language models
Large language models provide nearly identical portfolio advice across twenty-one countries despite local differences, following retail finance conventions rather than academic prescriptions and ignoring household balance sheets.
RePEc
- 25 Sep 20260cites
Financial Language Models as Applied Artificial Intelligence Systems for News-Based Trading under Market Frictions
Introduces a market-friction-aware framework that converts timestamped financial news into auditable trading decisions while accounting for execution timing, transaction costs, and liquidity constraints.
arXiv
- 25 Sep 20260cites
From Tone to Trajectory: Continuous Sentiment and the Shape of Monetary Policy Communication
The study shows that how monetary policy sentiment unfolds across a press conference, not just its average tone, predicts rate changes and shapes forecaster expectations at the ECB and Fed.
arXiv
- 25 Sep 20260cites
FinInteract: Benchmarking Clarification and Intent Integration in Ambiguous Financial Question Answering
A benchmark reveals that language models answer financial questions above 90 percent with clarification but only 28.9 percent when they must elicit it themselves, exposing model ambiguity resolution.
arXiv
- 25 Sep 20260cites
FinRankGRPO: Optimizing LLMs for Listwise Financial Asset Ranking via Group Relative Policy Optimization
Develops a two-stage framework that fine-tunes language models for listwise asset ranking using Spearman rank correlation rewards, achieving a Sharpe ratio of 0.636 on asset allocation.
arXiv
- 25 Sep 20263fanfare
LLM-Based Semantic Surprises in FOMC Communication: Asset Prices and Financial-Market Stress
Semantic surprises extracted from Federal Reserve statements predict subsequent financial-stress dynamics and reduce forecast error by up to 23%, particularly when initial stress is high or during recessions.
SSRN
- 16 Apr 20264cites
Transforming the Voice of the Customer: Large Language Models for Identifying Customer Needs
Large Language Models are streamlining the process of identifying customer needs, letting analysts concentrate on more valuable work while still delivering precise insights.
arXiv
- 28 Dec 2025105shares
Twitter Sentiment and Financial Trends
A new financial sentiment index derived from Twitter data shows strong links to market conditions and can forecast stock market returns, particularly in response to changes in U.S. monetary policy.
SSRNFeatured 2×
- 14 Dec 20251cites
Measuring Corruption from Text Data
An automated corruption index using Brazilian municipal audit reports is efficient and more reliable than manual methods in detecting corruption.
arXiv
- 14 Dec 20252cites
Reasoning Models Ace the CFA Exams
An evaluation of reasoning models on CFA mock exams shows that models like Gemini 3.0 Pro and GPT-5 perform well, achieving high pass rates in professional testing.
arXiv
- 1 Dec 20250cites
Standard Occupation Classifier - A Natural Language Processing Approach
A project successfully developed a natural language processing model that classifies job ads with 72% accuracy by using an ensemble approach.
arXiv
- 12 Nov 20251cites
Measuring economic outlook in the news
We build an interpretable, privacy‑friendly sentiment indicator from Swiss news using ML and LLMs, and it improves short‑term GDP forecasts.
arXiv
- 27 Oct 20250cites
Aligning Multilingual News for Stock Return Prediction
Uses optimal-transport to align English–Japanese stock news, producing clearer signals that better predict returns.
arXiv
- 27 Oct 20251cites
Black Box Absorption: LLMs Undermining Innovative Ideas
LLM platforms can quietly absorb users’ ideas, creating power imbalances; the paper proposes governance and technical fixes to protect creators.
arXiv
- 27 Oct 20250cites
Integrating Transparent Models, LLMs, and Practitioner-in-the-Loop: A Case of Nonprofit Program Evaluation
Combining transparent decision trees, LLMs, and practitioner input yields accurate, explainable case-level predictions for public and nonprofit programs.
arXiv
- 27 Oct 20251cites
News-Aware Direct Reinforcement Trading for Financial Markets
- News-RL Trading - News-Driven Trading - News-Based RL - RL for News Trading - News-Powered RL If you want the shortest single choice: News-RL Trading.: Short summary: Feeding news sentiment extracted by large language models together with raw price and volume into a sequence-model reinforcement learning system boosts cryptocurrency trading performance, removing the need for handcrafted trading rules.
arXiv
- 27 Oct 20254shares
Abstract Classification: SVM vs BERT vs GPT-3.5
SVM vs BERT vs GPT-3.5: Compares SVM, SPECTER, BERT, and GPT-3.5 for classifying abstracts: BERT performs best, while GPT-3.5 is inconsistent with limited training data.
RePEcFeatured 8×
- 9 Oct 20252cites
From Classical Rationality to Contextual Reasoning: Quantum Logic as a New Frontier for Human-Centric AI in Finance
The potential of quantum logic in advancing artificial intelligence applications in financial modeling is discussed.
arXivIn Quantum Economics and Finance
- 9 Oct 20254cites
From News to Returns: A Granger-Causal Hypergraph Transformer on the Sphere
The research suggests the CSHT, a new architecture for financial time-series forecasting that considers the impact of financial news and sentiment on asset returns, providing robust generalisation across market regimes and clear attribution pathways.
arXivIn Proceedings of the 6th ACM International Conference on AI in Finance
- 3 Oct 20252cites
Extracting the Structure of Press Releases for Predicting Earnings Announcement Returns
The research explores the predictive power of textual features in earnings press releases on stock returns, concluding that press release content is as informative as earnings surprise, with FinBERT being the most predictive.
arXivIn Proceedings of the 6th ACM International Conference on AI in FinanceFeatured 2×
- 22 Sep 20252cites
The (Short-Term) Effects of Large Language Models on Unemployment and Earnings
Large Language Models like ChatGPT have boosted earnings for workers in certain occupations without affecting unemployment rates, indicating they enhance income rather than replace jobs.
arXiv
- 22 Sep 20252cites
Context-Aware Language Models for Forecasting Market Impact from Sequences of Financial News
The study suggests using large language models to process financial news and small models to encode historical context, resulting in improved simulated investment performance.
arXiv
- 22 Sep 202518cites
Trading-R1: Financial Trading with LLM Reasoning via Reinforcement Learning
The article presents Trading-R1, a finance-focused AI model that aligns with trading principles, showing it offers better risk-adjusted returns and fewer drawdowns than other models.
arXiv
- 29 Aug 202523cites
FinReflectKG: Agentic Construction and Evaluation of Financial Knowledge Graphs
Financial Knowledge Graph Construction: The paper introduces a large-scale financial knowledge graph dataset from SEC 10-K filings of S and P 100 companies, with a reflection-agent-based mode providing the best balance of efficiency, accuracy, and reliability.
arXivIn Proceedings of the 6th ACM International Conference on AI in Finance