Context-Based Question Answering Models

• Context-Based Question Answering models are neural architectures that leverage rich contextual inputs from text, dialog history, and multimodal sources to generate precise answers.
• They employ techniques such as dynamic context selection, multi-granular fusion, and joint modeling to extract, generate, or classify answers effectively.
• Empirical benchmarks highlight state-of-the-art performance on datasets like SQuAD and CoQA, although challenges remain in scalability, noisy inputs, and complex multi-hop reasoning.

Context-Based Question Answering (CBQA) Models are a diverse class of neural architectures designed to predict answers to questions by leveraging explicit contextual signals in the input—often in the form of supporting passages, dialog history, structured knowledge, or external facts. These models have become central across extractive, generative, and multi-hop QA tasks spanning textual, knowledge-graph, conversational, and multimodal domains.

1. Formal Definition and Taxonomy

CBQA models operate on instances comprising a context $C$ (such as a passage, set of documents, knowledge subgraph, dialog history, or multimodal scene) and a question $Q$. The objective is to produce an answer $A$ that is either a span within $C$ (extractive), a generated sequence (generative), or a classification over candidate choices (multiple-choice):

• Extractive Formulation:

$P(A|C, Q) = P(s, e|C, Q)$, where $(s, e)$ denote start and end indices of the answer span.

• Generative Formulation:

$P(A|C, Q) = \prod_{i} P(a_i | a_{<i}, C, Q)$

CBQA subsumes traditional machine reading comprehension (MRC), conversational QA, knowledge-based QA, and context-rich video or multimodal QA (Muneeb et al., 29 Nov 2025).

Model families include:

• Span extractors: BERT-based, RoBERTa, ELECTRA, etc.
• Generative models: T5, GPT-style architectures.
• Hybrid models: Retrieve/generate context, then use extractive heads.
• Graph-fused models: Integrate knowledge graphs via GNNs or transformer-GNN hybrid layers.
• Dialog/Conversational models: SDNet, BERT-CoQAC, etc. (Zhu et al., 2018, Zaib et al., 2021)
• Multimodal: VideoQA pipelines such as VidCtx (Goulas et al., 23 Dec 2024).

Contexts can be lengthy (up to thousands of tokens), involve dialog history, or be sourced from domain-specific resources (e.g., biomedical literature, instructional documents, videos).

2. Model Architectures and Context Integration Strategies

CBQA models are characterized by explicit mechanisms to incorporate and reason over context:

• Sentence/block attention: BLANC’s block-attention predicts not just answer spans but supporting context regions (Seonwoo et al., 2020).
• Multi-granular fusion: SDNet concatenates N prior (Q,A) pairs to construct a “super-question,” integrating context via inter-attention and self-attention at multiple layers (Zhu et al., 2018).
• Context selection: Models often employ learned or heuristic selectors for minimal context (e.g., selecting top-k sentences) to improve efficiency and robustness, as shown by sentence selection modules (Min et al., 2018).
• Structured context fusion: Graph-based QA models use dynamic-hop subgraph retrieval and layer-wise fusion via cross-modal attention to inject KG-derived facts alongside textual encodings (Lu et al., 2022). Joint transformer-GNN stacks like FuseQA attend over both modalities at every layer.
• Conversational history encoding: BERT-CoQAC selects only the most relevant previous QA turns, models them with special marker embeddings in BERT’s input, and shows that relevance-based selection outperforms passing all history (Zaib et al., 2021).
• External fact augmentation: On science QA, FusionMind demonstrates that supplementing transformer inputs with hand-picked, highly relevant facts yields larger gains than simply adding a knowledge graph (Verma et al., 2023).
• Multimodal context: VidCtx passes both visual frames and “distant,” question-aware textual captions as context for video QA, using LMM architectures and pooling frame-level predictions (Goulas et al., 23 Dec 2024).

A central CBQA innovation is the dynamic and selective use of context, balancing efficiency, noise suppression, and supporting varied reasoning types (coreference, multi-hop, commonsense).

3. Training Objectives and Optimization

CBQA models employ task-specific loss functions, typically based on cross-entropy objectives adapted for context selection, span extraction, and multi-type answer supervision:

• Multi-task objectives: BLANC introduces a joint loss $L_{total} = (1-\lambda) L_{QA} + \lambda L_{CP}$, where $L_{CP}$ is a block-attention derived binary cross-entropy over token-level context membership (Seonwoo et al., 2020).
• Joint posterior modeling: Hybrid models like the BERT–BiDAF joint probability predictor model $P(A, X_1, X_2)$, explicitly chaining answerability and span selection in a causal fashion (Yang et al., 2019).
• Context selector and answer losses: Generative context-pair selection in multi-hop QA separately trains a prior ($L_{prior}$), question-generation ($L_{gen}$), and answer extraction ($L_{ans}$) loss, ensuring robust reasoning (Dua et al., 2021).
• Multi-choice and graph-fusion: Structured knowledge fusion models train with standard or weighted cross-entropy over answer candidates, backpropagating through both text and graph branches (Lu et al., 2022, Xu et al., 2020).
• Conversational span losses: Conversational models optimize span prediction (with softmax over tokens), sometimes incorporating special heads for yes/no and unknown classes (Zhu et al., 2018).
• Closed-book marginalization: Two-stage context generation frameworks marginalize the answer likelihood over $k$ generated contexts to mitigate hallucination and context uncertainty in open-domain settings (Su et al., 2022).

Optimization commonly leverages Adam or AdamW variants, with modern architectures using variational dropout, layer regularization, and, for large models, freezing pretrained encoder weights while learning only fusion or projection layers.

4. Empirical Benchmarks, Evaluation, and Performance

CBQA models have been extensively benchmarked across MRC, dialog, knowledge-based, multi-hop, and multimodal QA datasets:

• Span-extraction: Models trained on SQuAD v2 or v1 have achieved up to 43% (ELECTRA-large SQuAD2) average accuracy across eight diverse datasets (Muneeb et al., 29 Nov 2025), with context length and model size directly impacting both speed and accuracy.
• Conversational QA: SDNet set new state-of-the-art F1 on CoQA (76.6% single, 79.3% ensemble), surpassing prior baselines by >1.6 points (Zhu et al., 2018). In QuAC, context-aware architectures gained ~9 F1 points by incorporating dialog history, though performance still lags humans by >20 F1 (Choi et al., 2018).
• Commonsense and science QA: Structured context models using combined knowledge graphs and external definitions (e.g., DEKCOR/ALBERT+KCR) reach 80.7% on CommonsenseQA, with ablations showing that triple and description context each provide additive benefits (Xu et al., 2020).
• Multi-hop reasoning: Generative context selection offers improved adversarial robustness (+4.9 F1 over discriminative pipelines on adversarial HotpotQA dev) by enforcing question-to-context explicability (Dua et al., 2021).
• Factual vs. KG context: Simple models see >14 points F1 gain from appending relevant facts compared to modest (< 3 points) gains by adding generic KG information (Verma et al., 2023).
• VideoQA/multimodal: VidCtx, as a training-free, context-aware framework, achieves SOTA or near-SOTA on NExT-QA, IntentQA, and STAR among open LMM-based methods, with accuracy up to 70.7% (Goulas et al., 23 Dec 2024).

Performance drops with increased answer span length, context complexity, and domain shift unless models are explicitly adapted or fine-tuned. Long-context architectures (BigBird, Longformer, LED) mitigate resource scaling issues. Dialog and multi-hop settings remain challenging due to coreference and context aggregation demands (Muneeb et al., 29 Nov 2025, Choi et al., 2018).

5. Analysis of Context Modeling, Bias Mitigation, and Error Modes

Robust context modeling in CBQA is critical for countering spurious correlations, answer ambiguity, and adversarial attacks:

• Selective context extraction: Minimal sentence selection reduces both training/inference cost and adversarial susceptibility (Min et al., 2018).
• Auxiliary context prediction: Predicting supporting context blocks forces the model to disambiguate among identical answer strings occurring in distinct locations, with gains amplifying as answer multiplicity increases (Seonwoo et al., 2020).
• Generative formulations: Generative context-pair selection constrains the model to explain every aspect of the question (via $P(q|c_{ij})$), reducing vulnerability to superficial cues and improving robustness (Dua et al., 2021).
• Dialog history selection: Passing entire prior history introduces noise; explicit selection of relevant past turns (cosine similarity to current Q) enhances answer precision (Zaib et al., 2021).
• Structured knowledge fusion: Deep, per-layer fusion (Transformer–GNN cross attn) avoids the bottleneck and loss of mutual dependency encountered in single-token representations while allowing dynamic subgraph construction (Lu et al., 2022).
• Fact augmentation: High-precision, domain-specific facts injected as context greatly outperform generic structured information for specialized domains (Verma et al., 2023).

Common failure modes include missed coreferences, excessive context noise, shallow pattern-matching when structured signals are weak, and degraded performance on long or complex answers.

6. Contemporary Challenges and Future Directions

Several open problems remain in CBQA:

• Scalability: O(N)-scaling context selection and training-free inference (VidCtx) are increasingly favored for large, multimodal contexts (Goulas et al., 23 Dec 2024).
• Multi-hop and evidence aggregation: Models capable of multi-block or multi-hop selection (e.g., m-block BLANC, dynamic-hop FuseQA) are essential for compositional and scientific QA (Dua et al., 2021, Seonwoo et al., 2020, Lu et al., 2022).
• Context-source quality: Domain facts are currently the most effective external context, but scaling automatic selection for diverse settings is non-trivial (Verma et al., 2023).
• Hybrid architectures: Deeply integrated retrieval-generation, joint modeling of unstructured (text) and structured (KG, visual) signals, and context marginalization are active research frontiers (Su et al., 2022, Xu et al., 2020).
• Dialog and multi-turn contexts: Handling long dialog histories with sparse relevance, coreference, and entity tracking remains unsolved at human parity (Choi et al., 2018, Zaib et al., 2021).
• Error mitigation: Sensitivity to adversarial/contextual distractors and noise sources (irrelevant history, ambiguous entity linking) prompts interest in more robust joint modeling and explicit evidence supervision.

Ongoing research seeks layer-wise co-training, dynamic or learned context selection, hierarchical fusion, and more efficient architectures to bridge the substantial gap to human-level contextual reasoning.

7. Practical Recommendations and Model Selection

Empirical and ablation results across CBQA research yield the following recommendations (Muneeb et al., 29 Nov 2025):

• Factor in context length and complexity: Use long-context models (BigBird/LED) for large passages, distilled/mobile architectures for low-latency, factual Q&A.
• Prioritize domain-adaptation: Fine-tuning or pre-selecting domain-specific models (e.g., BioBERT, CPGQA-ELECTRA) is necessary for substantial domain shifts.
• Match answer type to architecture: For short, unambiguous answers, standard span-extractors suffice; for longer/multi-sentence spans, consider sequence-to-sequence or post-processing hybrid models.
• Profile dataset characteristics: Analyze average context and answer lengths, vocabulary, and dialog/multi-hop requirements before model selection.

Sophisticated CBQA performance depends critically on aligning architecture strengths with dataset and application demands, employing context selection, dynamic fusion, and multi-level attention in architectures specifically tailored to the unique challenges of each setting.