Targeted Question-Answering Protocol

• Targeted Question-Answering is a family of protocols that explicitly limits evidence, reasoning paths, and answer forms based on domain, question type, or modality.
• Protocol decomposition transforms raw data into intermediate representations like AMR graphs and SPARQL queries, enhancing answer validation and reproducibility.
• Evidence targeting across modalities and interactive, adaptive strategies improve retrieval precision and decision-making in specialized applications such as biomedical and multimodal QA.

Targeted question answering denotes QA protocols in which the admissible evidence, reasoning procedure, and answer form are explicitly constrained by a particular question type, domain, modality, or downstream decision objective. Across recent work, this targeting appears as domain grounding in biomedical literature retrieval and pilot documentation, question-conditioned evidence construction in visual, video, and ECG QA, progressive information collection for classification, and abstract synthesis over controlled document scopes (Lamurias et al., 2020, Arnold et al., 2020, Wu et al., 2019, Pham et al., 7 May 2025, Mishra et al., 2024, Wang et al., 11 May 2026). Rather than treating QA as a single uniform input-output mapping, these systems specify how questions are formulated, which evidence is eligible, how intermediate representations are built, and how answers are validated or revised.

1. Definitional scope and protocol families

Across the literature, targeted QA is best understood as a family of protocols for answering within bounded semantic, evidential, or operational regimes. Some systems target a restricted corpus, such as PubMed-indexed biomedical articles cited in community answers or procedure-level units in aircraft manuals (Lamurias et al., 2020, Arnold et al., 2020). Others target a question-conditioned subset of a multimodal input, as in image captions tailored to a specific visual question, frame descriptions conditioned on a video question, or ECG prefixes aligned to lead-specific clinical queries (Wu et al., 2019, Romero et al., 2024, Pham et al., 7 May 2025). A third line targets the decision process itself, by asking follow-up questions, using multi-round interaction, or routing each question to a different answering strategy (Mishra et al., 2024, Lei et al., 2023, Hoveyda et al., 2024).

Protocol family	Representative papers	Defining constraint
Domain- and source-grounded QA	(Lamurias et al., 2020, Arnold et al., 2020, Bansal et al., 2021)	Evidence restricted to a bounded corpus such as cited PubMed papers, FCOM procedures, or subroutine-local context
Question-conditioned evidence construction	(Wu et al., 2019, Romero et al., 2024, Pham et al., 7 May 2025)	Visual or physiological evidence is rewritten into question-relevant captions, descriptions, or prefix embeddings
Interactive and adaptive QA	(Mishra et al., 2024, Lei et al., 2023, Hoveyda et al., 2024)	The protocol asks follow-up questions, exchanges feedback, or selects a strategy per question

This suggests that “targeted” is not a single architecture class but a protocol property. A system is targeted when it narrows either the evidence space, the question space, the answer space, or the reasoning path in a way that is explicit and task-governed.

2. Protocol decomposition and intermediate representations

A common feature of targeted QA systems is explicit decomposition into stages. AnswerQuest turns a multi-paragraph document into a catalog of Q&A items by splitting the document into sentences, detecting candidate answer spans, generating a question for each span, building a local 3-sentence passage, and then using QA as both validator and answer extractor; the paper characterizes this design as “QG proposes, QA verifies” (Roemmele et al., 2021). This decomposition is procedural rather than jointly learned, but it makes answerability a first-class protocol decision.

TAT-LLM makes the decomposition more formal by defining a step-wise pipeline over hybrid tables and text: Extractor, Reasoner, and Executor. For TAT-QA and TAT-DQA, the protocol is extended with question type and scale prediction, so that answering proceeds through evidence extraction, equation or rule generation, deterministic execution, and unit normalization rather than direct answer generation (Zhu et al., 2024). In the same spirit, EDUQA grounds educational QA in a Dynamic Concept Network whose nodes store attribute-level question-answer tuples and whose edges encode pedagogical relations; inference performs longest-prefix entity matching and then ranks either node attributes or inter-entity relations with a mixed statistical and semantic score,

$$Sim_{overall} = (1-\delta)\,Sim_{statistic} + \delta\,Sim_{semantic}$$

(Agarwal et al., 2019).

Knowledge-graph QA systems exhibit a closely related protocol logic. Q²Forge begins with KG configuration, schema extraction, and competency-question generation, then translates a question into SPARQL through KG-aware context retrieval, syntax-checked query generation, endpoint execution, and iterative refinement; semantic adequacy is assessed by an LLM-as-a-judge that assigns a score from 0 to 10 and provides explanations (Taghzouti et al., 19 May 2025). In each case, the intermediate representation—AMR graph, concept network, executable equation, or SPARQL query—is not incidental. It is the mechanism by which the protocol constrains what counts as an admissible answer.

3. Evidence targeting across modalities

In text-centered systems, targeted QA often begins by constraining document retrieval. BiQA extracts question-PMID pairs from answers in Biology Stack Exchange, Medical Sciences Stack Exchange, and Reddit’s r/nutrition when those answers cite PubMed-indexed articles, thereby creating supervision specifically for the retrieval stage of biomedical QA (Lamurias et al., 2020). A parallel strategy appears in aircraft documentation QA, where XML manuals are segmented into individual procedures, indexed with BM25F, and then passed to a BERT-based extractive reader; the retriever narrows the search space for both latency and relevance reasons (Arnold et al., 2020). A different but related domain targeting appears in software engineering QA, where the unit of context is a single subroutine and its associated artifacts rather than a general corpus (Bansal et al., 2021).

In multimodal systems, evidence targeting frequently takes the form of question-conditioned evidence construction. In VQA, question-relevant captioning uses question-attended image features and gradient-based caption selection so that the generated caption emphasizes the attributes, counts, and relations needed for the current question; on validation, question-relevant generated captions improve performance over question-agnostic captions, 65.8 versus 64.6 (Wu et al., 2019). Q-ViD follows the same principle for video QA by uniformly sampling 64 frames, prompting InstructBLIP with “Provide a detailed description of the image related to the question: {Q}”, concatenating the resulting frame captions in temporal order, and then letting an LLM answer the multiple-choice question from that question-dependent video description (Romero et al., 2024). Q-Heart applies analogous targeting to physiological signals: an adapted ECG encoder produces a global ECG embedding, ET-Mapper turns it into 12 ECG-aware prefix embeddings aligned with the 12 leads, and the protocol retrieves the top-3 similar clinical reports by cosine similarity to supply additional grounding for ECG-QA (Pham et al., 7 May 2025).

Interactive sketch QA makes the targeting even more explicit. In ISQA, the sender sees the image but not the question, the receiver sees the question but not the image, and multi-round communication proceeds through sketches plus weighted bounding-box feedback. The receiver’s feedback map indicates which regions of the current sketch are relevant to the question, so later rounds selectively add information where the question requires it rather than redrawing the scene generically (Lei et al., 2023). This suggests a broader principle: targeted QA often improves when the protocol transforms raw evidence into a question-specific representation before final answer generation.

4. Supervision and dataset construction

A large part of targeted QA research concerns how to create supervision that reflects the target regime. BiQA addresses the biomedical retrieval bottleneck by harvesting supervision from naturally occurring forum behavior, yielding 7,396 questions with PMIDs and 14,133 question-article pairs in the detailed corpus table, while also noting a discrepancy with the abstract and conclusion counts of 7,453 questions and 14,239 pairs (Lamurias et al., 2020). The paper further shows that forum-cited articles are significantly more semantically related to their paired questions than random biomedical articles, with $p < 0.001$ in all three forums, supporting the use of citation-supported answers as noisy but meaningful retrieval supervision.

Graph-guided procedural QA pushes supervision generation toward exhaustive semantic coverage. Procedural texts are converted into AMR graphs for single-step semantics and action flow graphs for cross-step temporal structure; graph traversals and transformations then generate role-specific, instruction-level, polarity, and temporal QA pairs in a controllable way (Pham et al., 2024). The paper’s main empirical claim is that semantic coverage, rather than syntactic diversity alone, is the key indicator for downstream QA performance, and that small models trained on these graph-generated corpora can exceed GPT-3 and ChatGPT on the target task (Pham et al., 2024).

Other systems use weaker but still structured supervision. The subroutine QA system constructs 10.9 million question/context/answer tuples from rules extracted from empirical studies of developer questions, turning recurring software-comprehension intents into large-scale synthetic training data (Bansal et al., 2021). iTRI-QA instead adopts a smaller, expert-seeded workflow: 50 curated QA pairs with PMID, DOI, and category labels are used to fine-tune Llama-3.2 models, and evaluation on 100 augmented samples shows that evaluation loss decreases as training size grows, reaching 9.27 for the 1B model and 9.13 for the 3B model at 25 QA pairs (Liu et al., 27 Jan 2025). Q²Forge extends this logic to knowledge graphs by generating competency questions and then validating corresponding SPARQL queries, producing exportable reference Q²sets rather than only runtime answers (Taghzouti et al., 19 May 2025).

Taken together, these systems treat dataset construction as part of the QA protocol itself. In targeted QA, supervision is rarely a neutral background resource; it is often a designed artifact that encodes what kinds of questions, evidence, and answer structures matter in the target environment.

5. Interaction, adaptation, and real-time operation

Some targeted QA protocols do not assume that the initial question and evidence are sufficient. GuideQ treats partial-information classification as an information-acquisition loop: a classifier predicts the top-3 labels, occlusion identifies representative keywords for those labels, an LLM asks a guided follow-up question, a QA model extracts the answer from held-out context, and the classifier is rerun on the augmented text (Mishra et al., 2024). On Symptom2Disease, this raises F1 from 61.1 to 79.7 with a BERT classifier and from 64.7 to 86.8 with DeBERTa, indicating that targeted questioning can improve downstream decisions when the initial evidence is incomplete (Mishra et al., 2024).

ISQA generalizes interaction to multimodal communication. The sender and receiver operate under asymmetric information, and multi-round sketch exchange is beneficial primarily when the communication budget is moderate: the paper reports that the gain from multi-round interaction is most significant when complexity $B \in (0.05N, 0.3N)$, while moving from two rounds to three yields only minor additional improvement (Lei et al., 2023). This highlights a specifically protocol-level tradeoff between interaction budget, interpretability, and answer accuracy.

Operational systems introduce further forms of adaptation. The aircraft-documentation assistant uses a Rasa dialog engine that routes question intents to retrieval and QA modules and, on negative user feedback, proposes the best answer from the next-ranked document (Arnold et al., 2020). Ocean-Q shifts almost all neural computation offline by pre-generating a large “ocean” of QA pairs and then answering live questions with sparse retrieval and lexical matching; on SQuAD-open it is 4X faster than DENSPI-Hybrid, 0.28 versus 1.24 seconds per query, with only a 3+% accuracy drop (Fang et al., 2020). REALTIME QA defines a weekly protocol for current-world questions, releasing about 30 questions every Saturday at 3 am GMT and showing that GPT-3 with Google Custom Search retrieval reaches 66.5 accuracy on original multiple-choice evaluation, compared with 44.9 for closed-book GPT-3 (Kasai et al., 2022).

AQA makes the adaptation rule explicit by casting strategy selection as a contextual multi-armed bandit over QA graphs. With reward

$r_t = \beta \cdot P_t - (1-\beta)\cdot T_t,$

where $P_t$ is performance and $T_t$ is execution cost, the system learns when to use no retrieval, one-time retrieval, or iterative retrieval plus reasoning (Hoveyda et al., 2024). On the test set, AQA achieves overall F1 0.697 in the time-agnostic setting and 0.687 in the time-aware setting, versus 0.502 for a static GPTSwarm graph; the time-aware version is also faster overall, 8.89 versus 12.78 (Hoveyda et al., 2024). This suggests that targeted QA increasingly includes not only question-conditioned evidence selection but also per-question orchestration of the answering pipeline itself.

6. Evaluation, diagnostics, and unresolved challenges

Targeted QA has driven a corresponding shift in evaluation. ASTRA-QA replaces coarse answer similarity with topic-based scoring for abstract document QA. Each instance provides an answer topic set $A$, a curated unsupported-topic set $H$, and aligned evidence, allowing evaluation of both required coverage and unsupported additions. If $\hat{A}(Q,Y)$ is the set of topics extracted from a system response $Y$, the benchmark defines topic precision, recall, and F1 as

$p < 0.001$0

and separately scores curated hallucination topics through $p < 0.001$1 and $p < 0.001$2 (Wang et al., 11 May 2026). The benchmark shows that higher coverage often coexists with worse unsupported-topic behavior, and that method rankings are more stable and much cheaper to obtain than head-to-head comparisons (Wang et al., 11 May 2026).

Other targeted QA systems validate their protocols in equally task-specific ways. BiQA evaluates whether cited articles can be retrieved and whether they are semantically closer to their questions than random biomedical articles; it also shows that a TERM-PACRR model trained only on BiQA reaches performance close to expert-annotated BioASQ training, with the gap under 0.01 in 3 of 5 test batches (Lamurias et al., 2020). AnswerQuest evaluates whether generated Q&A lists help humans recover document content: across 38 AMT participants, answer accuracy rises from 14.74% before seeing the generated items to 50.26% after (Roemmele et al., 2021). Q-Heart evaluates exact-match accuracy on ECG-QA and reports 0.628 on the 10% test setting and 0.614 on the full test set, while noting that open-ended query questions remain harder than verify and choose questions (Pham et al., 7 May 2025).

The limitations reported across these works are strikingly consistent. In TAT-LLM, extractor errors dominate a 100-example FinQA analysis, with 48% wrong evidence and 15% missed evidence, indicating that deterministic execution is not enough when evidence selection is weak (Zhu et al., 2024). REALTIME QA shows that retrieval freshness is the main bottleneck for current-world QA; in a manual analysis of the best generation model over the first six weeks, 34% of errors are retrieval errors and 13% reading-comprehension errors, and the benchmark repeatedly exposes stale answers when evidence is insufficient (Kasai et al., 2022). BiQA acknowledges that votes are only a noisy confidence signal and that all PMIDs in an answer are treated as equally relevant even when the framework cannot determine which citation is primary (Lamurias et al., 2020). AnswerQuest notes incomplete coverage, imperfect question naturalness, and the absence of salience or discourse modeling (Roemmele et al., 2021). Q-Heart identifies weak numerical reasoning and limited biomedical specialization of a compact 1B instruct model as remaining barriers (Pham et al., 7 May 2025). A plausible implication is that the next generation of targeted QA protocols will require stronger evidence selection, explicit abstention or uncertainty mechanisms, tighter control over unsupported content, and richer domain-specific intermediate representations rather than larger end-to-end generators alone.