open research questions in LLM and NLP research

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i came across this amazing paper which discusses research areas and ideas that still need to be worked upon.

it’s a really comprehensive paper that details 14 open research topics, each with 3-4 research ideas that can and need to be worked on.

following is a list of the topics and ideas mentioned in the paper.

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1. Fundamental NLP

1.1 Multilinguality

• Low-resource machine translation

  • Developing small benchmarks for low-resource languages
  • Creating large training corpora for languages with limited web presence
  • Exploring manually curated parallel corpora
  • Using OCR for low-resource language data
  • Developing translation dictionaries using models of word formation

• Multilingual models that work well for all languages

  • Addressing inequality in performance across languages
  • Incorporating off-the-shelf LLMs into MT systems
  • Improving performance on languages with limited representation in training data

• Code-switching

  • Addressing challenges of large variation in code-switching phenomena
  • Developing methods for synthetic data generation
  • Evaluating existing LLMs on code-switched text across language combinations
  • Distinguishing highly similar languages and dialects

1.2 Reasoning

• Complex reasoning

  • Improving numerical reasoning capabilities
  • Enhancing logical reasoning skills
  • Developing grounded reasoning abilities
  • Advancing causal inference capabilities
  • Combining strengths of neural networks and symbolic AI
  • Integrating LLMs with external reasoning tools (calculators, interpreters, database interfaces, search engines)

• Responsible reasoning in social contexts

  • Developing models for moral reasoning in complicated scenarios
  • Incorporating different social contexts and cultural backgrounds in reasoning
  • Collaborating with domain experts and policymakers

• Formally defining reasoning and designing proper evaluation frameworks

  • Refining the definition of reasoning in the context of LLMs
  • Developing methods to test reasoning skills in the face of data contamination
  • Addressing Goodhart’s law in reasoning evaluation
  • Creating reliable metrics for multi-step reasoning evaluation

1.3 Knowledge Bases

• Automatic knowledge base construction

  • Improving knowledge coverage and factuality
  • Enhancing knowledge linking capabilities
  • Addressing out-of-distribution data challenges
  • Mitigating hallucination in knowledge graph completion

• Knowledge-guided NLP

  • Developing efficient and effective ways to interact with external knowledge bases
  • Exploring web browsing for knowledge acquisition
  • Improving customized knowledge base lookup methods

• Culture-specific knowledge and common sense

  • Understanding limitations of NLP models regarding cultural knowledge
  • Acquiring and representing knowledge encoding diverse cultural views
  • Developing methods to invoke cultural knowledge appropriately

1.4 Language Grounding

• Fusing multiple modalities

  • Developing efficient and effective methods for combining different modalities
  • Addressing challenges of competing modalities in multimodal models

• Grounding for less studied modalities

  • Exploring physiological, sensorial, or behavioral modalities
  • Integrating less-studied modalities with current multimodal LLMs

• Grounding “in the wild” and for diverse domains

  • Collecting and utilizing data from realistic “in the wild” settings
  • Adapting models to fewer data points or different types of data in diverse domains
  • Incorporating domain expertise for better problem setup understanding

1.5 Child Language Acquisition

• Sample-efficient language learning

  • Mimicking learning strategies of children for better generalization
  • Improving performance of NLP models while reducing required training data

• Language models as biological models for child language acquisition

  • Using neural models as biological models for human cognitive behavior
  • Comparing models’ learning curves with children’s age of acquisition for different words
  • Exploring phoneme-level acquisition and intrinsic rewards in language learning

• Benchmark development in child language acquisition

  • Creating new language acquisition benchmarks
  • Augmenting controlled experiments with large video datasets of children learning language

1.6 Non-Verbal Communication

• Non-verbal language interpretation

  • Analyzing non-verbal cues (facial expressions, gestures, body language)
  • Determining universal sets of expressions and gestures across modalities, contexts, and cultures

• Sign language

  • Addressing high variability in manual gestures for data curation and evaluation
  • Incorporating additional information (facial expressions, body pose, eye gaze)
  • Developing sign language generation for various scenarios

• Joint verbal and non-verbal communication

  • Representing, fusing, and interpreting verbal and non-verbal signals jointly
  • Developing language models for each modality and effective fusion methodologies

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2. Responsible NLP

2.1 NLP and Ethics

• Dual use:

  • Addressing the potential misuse of NLP technologies
  • Developing safeguards against malicious applications (e.g., deceptive text generation, misinformation campaigns)
  • Interdisciplinary collaboration to combat unethical uses of NLP

• Fairness:

  • Evaluating and mitigating bias in NLP models
  • Investigating dataset creation practices and their correlation with model bias
  • Developing stricter requirements for data creation to reduce inequalities

• Privacy:

  • Addressing concerns about access to user data through LLMs
  • Exploring privacy-preserving methods such as:
    • Differential privacy
    • Federated learning
    • Secure multi-party computation

• Attribution of machine-generated data:

  • Developing standard approaches for attribution in NLP-generated content
  • Addressing copyright and plagiarism issues in AI-generated text
  • Exploring membership inference techniques for identifying training data influence

2.2 Interpretability

• Probing:

  • Investigating internal representations of NLP models
  • Designing probing tasks to reveal linguistic and world knowledge
  • Identifying potential biases in model representations

• Mechanistic interpretability:

  • Uncovering underlying mechanisms in model decision-making
  • Extracting computational subgraphs from neural networks
  • Reverse engineering deep neural networks

• Human-in-the-loop approaches:

  • Incorporating human feedback to enhance model interpretability
  • Developing interactive explanation generation techniques
  • Exploring active learning for interpretability

• Reference-based text generation:

  • Improving reliability of generated text through step-by-step explanations
  • Developing methods to supply references or sources for model claims
  • Enhancing traceability of information in generated content

2.3 Green/Efficient NLP

• Model efficiency:

  • Improving attention mechanisms for better efficiency
  • Exploring sparsity in models to scale up width while reducing FLOPs
  • Developing mixture-of-experts architectures
  • Optimizing architectures for balance between economics, efficiency, and performance

• Efficient downstream task adaptation:

  • Developing methods for adapting pre-trained models with minimal parameter updates
  • Exploring techniques like prompt-tuning and prefix-tuning
  • Investigating parameter-efficient fine-tuning approaches

• Data efficiency:

  • Removing redundant or noisy data from training datasets
  • Developing effective methods for data deduplication in vast corpora
  • Exploring data curation techniques for very large datasets

2.4 NLP for Online Environments

• Combating misinformation:

  • Developing fact-checking technology across different languages and modalities
  • Utilizing network analysis to track the spread of false content
  • Exploring retrieval and knowledge-augmented methods for context verification
  • Addressing hallucinations and factual inconsistencies in LLMs

• Ensuring content diversity:

  • Addressing the amplification of majority voices in LLM-generated content
  • Developing techniques to preserve and promote diverse perspectives
  • Investigating methods to counteract the under-representation of marginalized groups

• Preventing mis- and over-moderation:

  • Developing nuanced content moderation techniques that consider context and cultural differences
  • Addressing the risk of unfairly deleting safe speech by minority groups
  • Investigating the impact of political interests on online content filtering
  • Developing methods to trace and analyze topics that are filtered or demoted online

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3. Applied NLP

3.1 NLP for Healthcare

Healthcare benchmark construction

• Strategies to create and scale-up health datasets
• Synthetic data generation for healthcare
• Data augmentation from existing data
• Metrics to measure fidelity of synthetic data compared with real data

Improving clinical communication

• Simplifying medical jargon for laymen
• Developing educational tools for healthcare professionals
• Providing personalized healthcare recommendations
• Developing advanced NLP models for medical dialogue systems
• Exploring ethical implications of NLP-driven communication in healthcare

Drug discovery

• Extracting and analyzing information from scientific literature, patents, clinical records
• Identifying and prioritizing drug-target interactions
• Discovering new drug candidates
• Predicting compound properties
• Optimizing drug designs

3.2 NLP for Education

Intelligent tutoring systems

• Generating targeted practice questions
• Explaining students’ mistakes in various subjects
• Developing human-in-the-loop checks for reliability
• Addressing challenges of diverse data, privacy concerns, and trustworthiness
• Improving evaluation mechanisms

Educational explanation generation

• Generating explanations for complicated questions or reading materials
• Developing explanations for automatic grading systems
• Addressing concerns of overreliance on AI models
• Balancing AI support with human teaching

Controllable text generation

• Generating memorable stories corresponding to students’ academic skill levels and interests
• Addressing domain diversity while pursuing controllability
• Developing reliable evaluation techniques for text generation with diverse control requirements
• Creating dedicated benchmarks and datasets for controlled text generation

3.3 Computational Social Science

Development of new abstractions, concepts, and methods

• Advancing NLP methods for computational social science (CSS)
• Developing customized, high-level text analyses for CSS
• Evolving evaluation paradigms to capture validity of LLMs as language generators
• Addressing challenges of large target label spaces in CSS tasks

Population-level data annotation and labeling

• Using LLMs to annotate data simulating human interactions
• Comparing LLM effectiveness with human annotations
• Addressing LLMs’ tendency for higher recall than precision in annotations

Multicultural and multilingual CSS

• Conducting large-scale, multilingual, and multicultural analyses
• Studying language evolution across cultures
• Analyzing value variations across cultures
• Addressing under-representation of minority communities and low-resource languages
• Developing temporal grounding for CSS research

3.4 Synthetic Datasets

Knowledge distillation

• Transferring knowledge from larger models to smaller models
• Utilizing LLM outputs as synthetic examples
• Transforming or controlling generated data for quality
• Emulating LLM behavior with smaller, focused models

Control over generated data attributes

• Developing robust, controllable, and replicable pipelines for synthetic data generation
• Optimizing prompts for specific data attributes
• Addressing challenges in specifying attributes through instructions or examples

Transforming existing datasets

• Applying changes to create semantically preserving new datasets
• Format change (e.g., converting HTML news articles to plain text)
• Modality transfer (e.g., generating textual descriptions of images or videos)
• Style transfer (e.g., translating writing style from verbose to concise)

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