Pun-RAG: A Retrieval-Augmented NLP Framework

• The paper demonstrates that integrating a dense FAISS retriever with a Punjabi GPT-2 model significantly improves factual grounding and reduces hallucinations in low-resource environments.
• Pun-RAG leverages a curated Punjabi knowledge base from diverse sources to boost cultural fidelity and domain-specific accuracy.
• Beyond Punjabi NLP, the approach inspires broader pun-aware retrieval pipelines that combine pun detection, gloss retrieval, and context-conditioned generation for humor interpretation.

Pun-RAG denotes a retrieval-augmented generation framework centered on pun-aware language processing. In its explicit named usage, it is the retrieval-augmented variant of PunGPT for Punjabi, combining a Punjabi GPT-2 model with a dense FAISS-based retriever over a curated Punjabi knowledge base to improve factual grounding and domain recall (Singh et al., 3 Aug 2025). In a broader technical usage within pun research, the same label has also been used to denote retrieval-augmented systems that first identify punning words, then retrieve relevant senses, glosses, pronunciations, or examples, and finally generate interpretations or explanations conditioned on that retrieved evidence (Liu et al., 2021).

1. Named system in low-resource Punjabi NLP

Pun-RAG was introduced alongside PunGPT2, Pun-Instruct, and Quantum-RAG in work on low-resource Punjabi language modeling (Singh et al., 3 Aug 2025). The motivating claim is that Punjabi, despite over 100 million speakers, remains effectively low-resource in modern NLP because multilingual systems dilute Punjabi vocabulary, underrepresent Punjabi tokens in pretraining, and struggle with culturally specific material such as religious scriptures, folk tales, regional news, and colloquial social media. PunGPT2 addresses this by training a Punjabi-native GPT-2-style decoder-only model from scratch on a 35 GB corpus spanning literature, religious texts, news, and social discourse; Pun-RAG extends that base model with retrieval.

The base generator in this system is a 124M-parameter GPT-2 architecture with 12 layers, hidden size 768, and 12 attention heads, trained with causal language modeling and a context length of 1024 tokens (Singh et al., 3 Aug 2025). The tokenizer is Punjabi-optimized byte pair encoding, yielding 45 million subword tokens. Pun-RAG preserves that generative backbone but no longer relies solely on parametric memory. Instead, it augments inference with retrieved passages drawn from a Punjabi knowledge base compiled from the same 35 GB corpus.

This design makes Pun-RAG a language-specific RAG architecture rather than a generic multilingual wrapper. The paper presents it as a practical answer to the fact that even a monolingual model trained on 35 GB may still hallucinate or lack detailed factual knowledge for specific queries. Retrieval is therefore used to ground the model in external Punjabi text rather than asking the base LLM to internalize all knowledge during pretraining (Singh et al., 3 Aug 2025).

2. Retrieval architecture and knowledge base construction

The paper defines Pun-RAG as “a retrieval-augmented variant of PunGPT” that uses a “dense FAISS-based retriever” to index a Punjabi knowledge base compiled from the pretraining corpus (Singh et al., 3 Aug 2025). Architecturally, the system consists of three components: a Punjabi GPT-2 generator, a dense retriever, and a FAISS index over passage embeddings. At inference time, the workflow follows the classic RAG pattern: a Punjabi query is encoded, top-$k$ relevant passages are retrieved from the index, and those passages are appended to the input before autoregressive generation.

Formally, the base LLM is trained under the standard causal factorization

$$p(x_1, \dots, x_T) = \prod_{t=1}^{T} p(x_t \mid x_1, \dots, x_{t-1}),$$

while the retrieval-augmented prompt is constructed by concatenating the user query with retrieved context passages (Singh et al., 3 Aug 2025). The paper explicitly states that “relevant passages are retrieved and appended to the model input.” It does not specify the exact FAISS index type, the embedding dimensionality, or the precise similarity metric beyond describing the retriever as dense and FAISS-based. The operational implication is that Pun-RAG is described more at the system level than as a mathematically complete retriever-training framework.

The Punjabi knowledge base is derived from the same corpus used for pretraining. The sources include news websites such as Ajit, Jagbani, and Daily Punjabi Tribune; folk tales and literature from Panjab Digital Library and Punjabi story collections; social media comments from Facebook, YouTube, and Twitter; religious texts such as Sri Guru Granth Sahib and SikhNet Gurbani; Punjabi University archives; and public datasets including Punjabi Wikipedia, OSCAR, and AI4Bharat (Singh et al., 3 Aug 2025). The preprocessing pipeline includes deduplication, HTML and noise removal, Unicode normalization for Gurmukhi and Shahmukhi, filtering of incomplete and nonsensical sequences, and language identification to retain true Punjabi content.

Within the same paper, Pun-RAG is situated beside two related systems. Pun-Instruct is a QLoRA-based instruction-tuned variant aimed at zero-shot and instruction-following performance, while Quantum-RAG is a more experimental hybrid retriever that combines BM25, dense retrieval, and quantum-inspired semantic matching (Singh et al., 3 Aug 2025). That positioning is important: Pun-RAG is the baseline retrieval-augmented system in the suite, whereas Quantum-RAG is the paper’s more novel retrieval variant.

3. Pun-RAG as a pun-aware retrieval design pattern

Outside Punjabi language modeling, the term has also been used more generically to describe pun-aware retrieval pipelines. A key precursor is pun interpretation through pun–gloss pair classification. In “A Dual-Attention Neural Network for Pun Location and Using Pun-Gloss Pairs for Interpretation,” pun interpretation is reframed as binary classification over pairs of a pun sentence and a WordNet gloss, with the top two glosses selected as the intended meanings (Liu et al., 2021). In that formulation, each WordNet sense gloss functions naturally as a retrievable document, and the pipeline already contains the core non-generative stages of a Pun-RAG system: pun location, sense scoring, and explicit use of pronunciation.

This broader usage is reinforced by work on context-situated pun generation. “Context-Situated Pun Generation” introduces CUP, containing 4.5k tuples of context words and pun pairs, and explicitly decomposes the task into pun-pair retrieval followed by generation (Sun et al., 2022). Human evaluation in that work shows that 69% of top retrieved pun words can be used to generate context-situated puns, the generation module yields successful puns 31% of the time given a plausible tuple of context words and pun pair, and the full pipeline with the top-1 retrieved pun pair generates successful puns 40% of the time (Sun et al., 2022). Those results make retrieval not an auxiliary convenience but a central modeling step.

A second important line is the incorporation of humor principles directly into retrieval and decoding. “A Unified Framework for Pun Generation with Humor Principles” models ambiguity, distinctiveness, and surprise through a context word and phrase selector, a generation model trained on non-pun sentences, and a label predictor that steers decoding (Tian et al., 2022). Although the paper is not framed as RAG, its context selector already performs a retrieval-like function: it searches a large non-pun corpus for phrases characteristic of one meaning but still compatible with the pun word. This suggests that Pun-RAG can be understood not merely as attaching a retriever to a generator, but as structuring retrieval around pun-specific signals such as gloss compatibility, phonetic similarity, semantic distinctiveness, and local-global surprise.

The field-wide rationale for this design is summarized in the pun-generation survey, which argues that retrieval-augmented generation is especially natural for puns because the task requires external lexical knowledge, homophone and sense inventories, templates, and rich examples (Su et al., 7 Jul 2025). Under that reading, Pun-RAG is both a named Punjabi system and a general architecture for pun-aware NLP.

4. Empirical performance

In the Punjabi setting, Pun-RAG is evaluated on PunjabiEval across translation, question answering, and summarization, using perplexity, training loss, ROUGE-L, and cultural fidelity (Singh et al., 3 Aug 2025). The reported values place Pun-RAG between the base monolingual generator and the more advanced variants.

Model	Perplexity	ROUGE-L
mBERT	45.2	28.7
MuRIL	42.1	30.9
PunGPT	2.24	37.4
Pun-RAG	2.10	38.5
Pun-Instruct	2.15	39.2
Quantum-RAG	2.05	40.1

Pun-RAG also improves training loss from 0.85 for PunGPT to 0.80, and improves cultural fidelity from 4.4/5 to 4.6/5 (Singh et al., 3 Aug 2025). In the same experimental suite, Pun-Instruct reaches 4.7/5 cultural fidelity and Quantum-RAG 4.8/5. The abstract further states that the model suite significantly outperforms strong multilingual baselines, specifically mBERT, mT5, and MuRIL, in perplexity, factuality, and fluency (Singh et al., 3 Aug 2025).

Within pun generation proper, retrieval-centered formulations also show measurable gains. In context-situated pun generation, the best end-to-end pipeline with neural retrieval and a T5 pretrain-then-finetune generator reaches a 40% success rate, outperforming other modeling variations but remaining 32% below the human success rate (Sun et al., 2022). This matters for the encyclopedia entry because it shows that retrieval is not only useful for factual QA in Punjabi; it is also productive in explicitly pun-oriented generation settings.

A broader implication is that Pun-RAG inherits the standard RAG trade-off between retrieval quality and generator quality. In the Punjabi paper, the gains from retrieval are modest but consistent. In pun-generation work, better retrieved pun pairs or glosses often dominate downstream success. Taken together, these studies indicate that the performance ceiling of Pun-RAG is closely tied to the quality, coverage, and structure of the retrieved evidence (Singh et al., 3 Aug 2025).

5. Limitations, reproducibility, and robustness

The Punjabi Pun-RAG paper is explicit about several limitations. Its knowledge base is derived from the same 35 GB corpus used for pretraining, so coverage gaps remain in specialized domains and under-represented dialects or communities (Singh et al., 3 Aug 2025). Because the corpus includes web and social-media text, misinformation and bias in indexed passages can propagate into retrieval-conditioned outputs. The paper also notes practical constraints: dense retrieval requires storing embeddings and running FAISS search, and the system was developed under limited GPU resources, specifically a single A100 MIG 3g.20GB. A further reproducibility limitation is that the retriever is described operationally, but the paper does not provide a detailed training regime or explicit loss for the dense retriever.

From a broader RAG perspective, low-level perturbation robustness is a serious concern. “Typos that Broke the RAG’s Back” shows that RAG systems can be highly vulnerable to minor textual inaccuracies such as inner-shuffle errors, keyboard typos, punctuation insertion, phonetic substitutions, and visual substitutions, with GARAG achieving high attack success rates against both retriever and reader components (Cho et al., 2024). A plausible implication is that Pun-RAG systems are especially exposed because punning frequently involves phonetic play, orthographic deviation, or stylized surface forms. The concrete design recommendation in that work is to keep a clean semantic backbone for retrieval and grounding, and apply punning as a controlled surface layer rather than storing only stylized or noisy variants in the indexed corpus (Cho et al., 2024).

The wider pun-generation literature points to additional constraints. Small datasets, subjectivity of humor, and incomplete lexical resources all limit retrieval quality and make automatic evaluation difficult (Su et al., 7 Jul 2025). In practical terms, Pun-RAG works best when retrieval is paired with explicit structural controls or discriminators rather than relying on raw similarity search alone.

6. Multimodal and future directions

Recent benchmarks suggest that Pun-RAG is likely to expand beyond text-only settings. In audio, APUN-Bench provides 4,434 audio samples annotated across pun recognition, pun word location, and pun meaning inference, and shows that current large audio LLMs exhibit substantial gaps across all three stages (Su et al., 19 Mar 2026). The paper’s decomposition closely matches a multi-stage audio Pun-RAG pipeline: detect whether a spoken utterance contains a pun, locate the pun word, then retrieve alternative words or senses from pronunciation dictionaries and sense inventories to support interpretation. This suggests that future Pun-RAG systems may require explicit phonetic retrieval, not only semantic retrieval.

In vision-language settings, the Pun Rebus Art dataset contains 1,011 captioned images of Chinese artworks and evaluates element identification, symbolic matching, and explanation generation (Zhang et al., 2024). The reported findings are that current vision-LLMs struggle, often producing biased and hallucinated explanations, while retrieval of specialized cultural knowledge appears likely to help. This is not itself a Pun-RAG implementation, but it motivates a multimodal version in which retrieval targets visual motifs, phonetic mappings, symbolic categories, and expert analyses.

A further direction comes from domain-specific RAG adaptation work. RAGen is a framework for generating question–answer–context triples with semantic chunking, hierarchical concept extraction, multi-chunk retrieval, and curated distractors (Tian et al., 13 Oct 2025). A plausible extension is to adapt the same machinery to pun corpora: concept extraction would identify pun-related lexical or phonetic devices, distractors would distinguish genuine puns from literal near-matches, and the resulting triples could train both retrievers and generators specialized for pun understanding and explanation.

The current literature therefore supports two complementary understandings of Pun-RAG. One is the named Punjabi system that couples PunGPT with a dense FAISS retriever over a curated Punjabi knowledge base (Singh et al., 3 Aug 2025). The other is a broader research program in which pun-aware systems retrieve glosses, homophones, context words, example sentences, symbolic motifs, or multimodal evidence before generating interpretations or new puns (Liu et al., 2021). In both senses, the central idea is the same: pun processing improves when ambiguous linguistic forms are not handled as isolated tokens, but as retrieval targets embedded in lexical, phonetic, cultural, and contextual knowledge.